Spext looks like a google doc but works like a powerful media editor

Spext

spext

RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. Contact Information:
Email: info@rtv.com
Number: +1 (565) 987-6543 RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. About RTV: Contact Information:
Email: info@rtv.com Number: +1 (565) 987-6543 info@rtv.com

Swish Returns: RTV's Musical Theater Spectacle Makes a Comeback

NEW YORK, New York, May 7th, 2024/Chainwire/-- Spectral is excited to announce its participation in Hugging Face's Expert Support Program. Spectral is collaborating with deep learning experts from Hugging Face to advance open-source models, datasets, and applications for the Onchain Agent Economy. How To Use Hugging Face Hugging Face plays a critical role across Spectral's organization. Through the Expert Support Program, Spectral regularly meets with the Hugging Face team to plan new use cases, strategize dataset construction, and develop training strategies. Earlier this year, Spectral launched Syntax, which is composed of a fine-tuned LLM orchestrator that routes queries between several tools, including search, Foundry, and an open-source model to generate Solidity code. Building Syntax required custom datasets for both finetuning and RAG, an original Solidity evaluation dataset written by elite smart contract developers, and experimentation across a wide range of open and closed-source models. Spectral's approach to dataset preparation, training, routing, and evaluation has drastically improved during this process, largely due to help from Hugging Face experts. Regular discussions on Spectral's approach mean that the company always receives impartial feedback on its decisions and can reevaluate its strategy as needed. This was important for both the finetuning and evaluation steps, which have now gone through several rounds of improvement thanks to the Hugging Face team. Spectral also relies on Hugging Face for inferences in production through dedicated Inference Endpoints. The fully managed infrastructure allows Spectral to iterate on new models quickly, easily update container and hardware configurations, autoscale with demand, and keep production costs low. Over the coming months, Spectral is excited to continue to open-source its work with onchain datasets and models that interact onchain through Hugging Face. Users can view Spectral's training dataset for the credit scoring challenge and follow its progress here. The Onchain Agent Economy Syntax is pioneering the accessible onchain Agent Economy, inviting users to select agents tailored for their specific tasks. Users can either interact with the foundational agent to generate solidity code or opt for one of the specialized agents, each adept in distinct tasks. For example, we recently launched Syntax MoonMaker, an agent that launches a memecoin project end to end and are soon launching Syntax- TestMachine, an agent that detects vulnerabilities in your smart contracts. Many such agents are currently in production, and upcoming releases of our product will allow users to create their own agents and monetize them on the Syntax network. This system relies on a vast suite of functions available to language models, open-source models for tasks like classification and image generation, fast and reliable blockchain infrastructure, and a trustless system to execute and verify each step. To solve these problems, we've begun developing the Inferchain, optimized to serve the rapidly growing demand for agents and verifiable inferences. Hugging Face Users Joining The Onchain AI Community The importance of open collaboration cannot be overstated. Like many other projects, Spectral would not exist without the work of open-source contributors. Spectral is greatly indebted to everyone who makes this work possible and believes this positive impact should be recognized. As part of this engagement, Spectral is focusing specifically on users powering the AI community. The top individual and small organization contributors on Hugging Face, measured by likes and downloads of their models and datasets, are eligible to register to claim a share of SPEC tokens starting the week of May 6th. Users can check eligibility by signing in with their Hugging Face account here. A snapshot of activity was taken last month, and eligible users will not need an existing wallet to claim. This airdrop is designed to recognize users for their critical work and enable new functionality for the next generation of AI tools. Criteria Spectral reviewed all individual contributors from organizations with less than 10 members that published models or datasets. From these, Spectral took a combined measure of activity from likes and downloads for both models and datasets. Then, Spectral identified the users with the most combined activity from both model and dataset publishers (11298 in both categories) which resulted in a final list of 20,004 unique huggingface users. Spectral is looking forward to engaging further with the open-source community and building the future of AI x web3! https://claims.spectrallabs.xyz/ About Spectral Spectral, a pioneer of the agent economy behind Syntax, is at the forefront of integrating AI with blockchain to democratize development in Web3. Spectral's mission is to simplify the creation and deployment of decentralized applications through autonomous Onchain Agents. Syntax, Spectral’s flagship product, translates natural language into Solidity code, enabling both novices and experts to build on the blockchain effortlessly. With a commitment to transparency and user empowerment, Spectral is shaping a future where anyone can participate in the blockchain revolution. Join Spectral in making this vision a reality and explore the potential of autonomous agents. For more information, visit https://spectrallabs.xyz. For more information, users can follow Spectral on Twitter and Discord. Contact Spectral Labs Team contact@spectral.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. NEW YORK, New York, May 7th, 2024/Chainwire/-- Spectral is excited to announce its participation in Hugging Face's Expert Support Program. Spectral is collaborating with deep learning experts from Hugging Face to advance open-source models, datasets, and applications for the Onchain Agent Economy. How To Use Hugging Face Hugging Face plays a critical role across Spectral's organization. Through the Expert Support Program, Spectral regularly meets with the Hugging Face team to plan new use cases, strategize dataset construction, and develop training strategies. Earlier this year, Spectral launched Syntax, which is composed of a fine-tuned LLM orchestrator that routes queries between several tools, including search, Foundry , and an open-source model to generate Solidity code. Foundry Foundry an open-source model an open-source model Building Syntax required custom datasets for both finetuning and RAG, an original Solidity evaluation dataset written by elite smart contract developers, and experimentation across a wide range of open and closed-source models. Spectral's approach to dataset preparation, training, routing, and evaluation has drastically improved during this process, largely due to help from Hugging Face experts. Regular discussions on Spectral's approach mean that the company always receives impartial feedback on its decisions and can reevaluate its strategy as needed. This was important for both the finetuning and evaluation steps, which have now gone through several rounds of improvement thanks to the Hugging Face team. Spectral also relies on Hugging Face for inferences in production through dedicated Inference Endpoints. The fully managed infrastructure allows Spectral to iterate on new models quickly, easily update container and hardware configurations, autoscale with demand, and keep production costs low. Over the coming months, Spectral is excited to continue to open-source its work with onchain datasets and models that interact onchain through Hugging Face. Users can view Spectral's training dataset for the credit scoring challenge and follow its progress here . here here The Onchain Agent Economy Syntax is pioneering the accessible onchain Agent Economy, inviting users to select agents tailored for their specific tasks. Users can either interact with the foundational agent to generate solidity code or opt for one of the specialized agents, each adept in distinct tasks. For example, we recently launched Syntax MoonMaker, an agent that launches a memecoin project end to end and are soon launching Syntax- TestMachine, an agent that detects vulnerabilities in your smart contracts. Many such agents are currently in production, and upcoming releases of our product will allow users to create their own agents and monetize them on the Syntax network. This system relies on a vast suite of functions available to language models, open-source models for tasks like classification and image generation, fast and reliable blockchain infrastructure, and a trustless system to execute and verify each step. To solve these problems, we've begun developing the Inferchain, optimized to serve the rapidly growing demand for agents and verifiable inferences. Hugging Face Users Joining The Onchain AI Community The importance of open collaboration cannot be overstated. Like many other projects, Spectral would not exist without the work of open-source contributors. Spectral is greatly indebted to everyone who makes this work possible and believes this positive impact should be recognized. As part of this engagement, Spectral is focusing specifically on users powering the AI community. The top individual and small organization contributors on Hugging Face, measured by likes and downloads of their models and datasets, are eligible to register to claim a share of SPEC tokens starting the week of May 6th. Users can check eligibility by signing in with their Hugging Face account here . here here A snapshot of activity was taken last month, and eligible users will not need an existing wallet to claim. This airdrop is designed to recognize users for their critical work and enable new functionality for the next generation of AI tools. Criteria Spectral reviewed all individual contributors from organizations with less than 10 members that published models or datasets. From these, Spectral took a combined measure of activity from likes and downloads for both models and datasets. Then, Spectral identified the users with the most combined activity from both model and dataset publishers (11298 in both categories) which resulted in a final list of 20,004 unique huggingface users. Spectral is looking forward to engaging further with the open-source community and building the future of AI x web3! https://claims.spectrallabs.xyz/ https://claims.spectrallabs.xyz/ https://claims.spectrallabs.xyz/ About Spectral Spectral , a pioneer of the agent economy behind Syntax, is at the forefront of integrating AI with blockchain to democratize development in Web3. Spectral's mission is to simplify the creation and deployment of decentralized applications through autonomous Onchain Agents. Spectral Spectral Syntax, Spectral’s flagship product, translates natural language into Solidity code, enabling both novices and experts to build on the blockchain effortlessly. With a commitment to transparency and user empowerment, Spectral is shaping a future where anyone can participate in the blockchain revolution. Join Spectral in making this vision a reality and explore the potential of autonomous agents. For more information, visit https://spectrallabs.xyz . For more information, users can follow Spectral on Twitter and Discord . https://spectrallabs.xyz Twitter Twitter Discord Discord Contact Spectral Labs Team contact@spectral.finance This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Spectral Labs Joins Hugging Face’s ESP Program: Advancing The Onchain x Open-Source AI Community

স্পেকট্রাল ল্যাবগুলি আলিঙ্গন মুখের ইএসপি প্রোগ্রামে যোগ দেয়: অনচেইন এক্স ওপেন-সোর্স এআই সম্প্রদায়কে এগিয়ে নিয়ে যাওয়া

Spectral Labs nimmt am ESP-Programm von Hugging Face teil: Förderung der Onchain x Open-Source-KI-Community

Spectral Labs se une al programa ESP de Hugging Face: avanzando en la comunidad de IA de código abierto Onchain x

Spectral Labs rejoint le programme ESP de Hugging Face : faire progresser la communauté d'IA Onchain x Open-Source

स्पेक्ट्रल लैब्स हगिंग फेस के ईएसपी कार्यक्रम में शामिल हुई: ऑनचेन एक्स ओपन-सोर्स एआई समुदाय को आगे बढ़ाना

Spectral Labs が Hugging Face の ESP プログラムに参加: オンチェーン x オープンソース AI コミュニティの発展

Spectral Labs, Hugging Face의 ESP 프로그램에 합류: Onchain x 오픈 소스 AI 커뮤니티 발전

Spectral Labs junta-se ao programa ESP da Hugging Face: Avançando a comunidade Onchain x Open-Source AI

Spectral Labs присоединяется к программе ESP Hugging Face: развитие сообщества искусственного интеллекта с открытым исходным кодом Onchain x

Spectral Labs, Hugging Face'in ESP Programına Katılıyor: Onchain x Açık Kaynak Yapay Zeka Topluluğunu Geliştirme

Spectral Labs tham gia chương trình ESP của Hugging Face: Thúc đẩy cộng đồng AI mã nguồn mở Onchain x

Spectral Labs 加入 Hugging Face 的 ESP 计划：推进 Onchain x 开源 AI 社区

NEW YORK, New York, March 26th, 2024/Chainwire/--**Syntax takes natural language and converts it into Solidity code, enabling individuals and enterprises to ship production grade smart contracts, arbitrage agents, NFTs, rollups, and more. Syntax marks the manifestation of the Agent Economy in Web3. Today, has launched Syntax, a new LLM enabling people to ship agents that automate onchain projects and tasks, thereby manifesting their Web3 product ideas. While still in beta, Syntax’s vision is to create an open onchain Agent Economy, where developers, individuals, and enterprises can for the first time select the agents they would want to delegate their Web3 tasks, projects, and product development to. These projects and products include production grade smart contracts, arbitrage agents, NFTs, rollups, and more. Spectral Labs Scaling web3 engineering efforts and shipping new onchain products is limited by the number of solidity developers, as well as enterprises’ ability to test and ship smart contract code in a fast and effective way without burning through engineering resources. Spectral has set out to solve this with its new LLM, Syntax, a development environment designed to manifest Web3 product ideas by converting natural language into Solidity code that can be deployed instantly onchain. For professional developers, Syntax can accelerate and optimize bandwidth by writing large libraries and components. Syntax provides an Agentic experience, where users tell Syntax what they want to build, and Syntax writes and deploys the code onchain at the click of a button. Spectral defines an Onchain Agent as a set of instructions and code capable of deploying itself onchain and possessing a dedicated provisioned wallet for doing so. Loosely similar to custom instructions in ChatGPT, Syntax Agents also have their own identity and can autonomously figure out ways to interact with deployment infrastructure. With Syntax, users can compile, debug, and deploy AI-generated solidity code. Syntax gives users and the industry the ability to quickly test, ship and scale new products, as well as determining the usability and functional relevance of the code generated while also giving them control to quickly edit code snippets. Initially, Spectral’s Onchain Agents will be curated and built by Spectral, but soon every user will be able to create their own Agent and monetize it in future Syntax releases. "At Spectral, we're pioneering a future where autonomous Agents redefine what's possible onchain. With Spectral Syntax, we empower these agents to operate independently, making it simpler for everyone to bring their blockchain ideas to life." Sishir Varghese, Founder. What truly sets Syntax apart is its capability to deploy code directly onchain, executing deployments through Foundry by writing instructions that Foundry can process, including code compilation, onchain deployment, and signing transactions. Syntax is pioneering an accessible onchain Agent Economy, inviting users to select agents tailored for their specific Web3 tasks. Users can either interact with the foundational agent or opt for one of the specialized agents, each adept in distinct tasks and operations—like the Code Forker assistant, ideal for those aiming to fork and customize a codebase. Additionally, users can discern an agent's expertise and trustworthiness by reviewing its category listing and the popularity reflected through its user engagement. "Syntax by Spectral is at the forefront of merging AI with web3, pioneering the onchain agent economy and making it easier and more accessible for users to create their own blockchain products," said Josh Rosenthal, Partner, Polychain Syntax produces accurate and gas-optimized Solidity code for a variety of prompts ranging from low to medium complexity, and it outperforms baseline models on Spectral’s Solidity evaluation dataset. As Spectral’s modeling efforts continue to evolve, the company expects Syntax to deliver optimized code for increasingly complex problems. These Solidity models are explicitly designed to assist developers in writing optimized, modern, well-tested, and secure code. About Spectral , a pioneer of the agent economy behind Syntax, is at the forefront of integrating AI with blockchain to democratize development in Web3. Our mission is to simplify the creation and deployment of decentralized applications through autonomous Onchain Agents. Syntax, Spectral’s flagship product, translates natural language into Solidity code, enabling both novices and experts to build on the blockchain effortlessly. Spectral With a commitment to transparency and user empowerment, Spectral is shaping a future where anyone can participate in the blockchain revolution. Join us in making this vision a reality and explore the potential of autonomous agents with Spectral. For more information, visit /. For more information, users can follow Spectral on and . https://spectrallabs.xyz Twitter Discord Contact Spectral Labs Spectral info@spectrallabs.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Spectral Launches Syntax Enabling Web3 Users To Build Autonomous Agents And Deploy Onchain Products

Authors: (1) Michael Sorochan Armstrong, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (2) Jose Carlos P´erez-Gir´on, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada; (3) Jos´e Camacho, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (4) Regino Zamora, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada. Table of Links Abstract & Introduction Optimization of the Optical Interpolation Materials and Methods Results and Discussion Conclusion Appendix A: Proof of Hermitian Self-Adjoint product identity for Equidistant Time-Domain Measurements Appendix B: AAH ̸= MIN I in the Non-Equidistant Case Acknowledgments & References 5 CONCLUSION A convenient mathematical expression to solve for the spectrum of irregularly sampled time-domain data has been demonstrated in a way that satisfies the conditions for an inverse non-uniform Fourier transform. This methodology has been derived from cost function proposed in the original study, and via its idealised quadratic form strongly suggests that its minimum is unique and convex. Using a randomly sample time series from data with few missing values, the observed relative error is on the same order of magnitude as the most current solution to this problem [10]. Upon cross-validation with contiguous blocks, we report a relative error that is much higher, and suggest this is a more critical, but informative lens through which to view interpolation problems. he proposed formula will find use in a variety of signal processing applications, where the use of an FFT is impractical given its assumption of a consistent time-domain density. The software package used in this work is available online: https://github.com/CoDaSLab/intrp infft 1d This paper is under CC 4.0 license. available on arxiv

Novel Mathematical Expression for Spectrum Determination of Irregularly Sampled Data

Authors: (1) Michael Sorochan Armstrong, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (2) Jose Carlos P´erez-Gir´on, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada; (3) Jos´e Camacho, Computational Data Science (CoDaS) Lab in the Department of Signal Theory, Telematics, and Communications at the University of Granada; (4) Regino Zamora, part of the Interuniversity Institute for Research on the Earth System in Andalucia through the University of Granada. Table of Links Abstract & Introduction Optimization of the Optical Interpolation Materials and Methods Results and Discussion Conclusion Appendix A: Proof of Hermitian Self-Adjoint product identity for Equidistant Time-Domain Measurements Appendix B: AAH ̸= MIN I in the Non-Equidistant Case Acknowledgments & References Abstract A simple least-squares optimization enables the determination of the spectrum for irregularly sampled data that is readily reconstructed using an adjoint transformation of the Non-Uniform Fast Fourier Transform (NFFT). This is an improvement upon previously reported iterative methods for such problems and is competitive in terms of time complexity with more recently proposed direct NFFT inversions when considering comparable matrix pre-computation steps. The software is highly portable, and available as a convenient Python package using standard libraries. Given its mathematical simplicity, however, it can be conveniently implemented on any platform. —Sensors, Digital processing, Non-uniformity correction, Remote monitor Index Terms 1 INTRODUCTION Remote sensor data is streamed from regions that are difficult to access for conventional measurements. Due to interruptions in either network connectivity, power or other environmental conditions there may be significant periods of time during which data acquisition is interrupted. This may not pose problems for ”binned” time-series analysis of the data, if the acquisition rate of the data is relatively high compared with the expected duration of a typical outage. Binning data does partially resolve the spatio-temporal lag of phenomena measured across different points for the same event which is assumed to take place within a predefined period of time. However regardless of whether or not missing data is present, this does not make use of the full acquisition rate of the instrument, which negates some benefits of remote measurement. Instead, it is possible to perform an analysis of the spectrum or frequency domain information of the remote measurements and reconstruct the original data based on parallel analysis of similar remote stations. Spectral analysis of irregularly sampled data is an active area of research, and is often referenced broadly as a leastsquares analysis, owing to a need for numerical estimation. This type of analysis commonly finds applications wherever there is an interruption in measurement - as is commonly seen in climate applications [1], and other environmental monitoring [2]. 1.1 Fourier transforms of irregularly sampled data A full spectrum of Fourier coefficients in can be calculated up to where the maximum frequency of is the Nyquist frequency; beyond which the sampling rate is not sufficient to warrant calculation of further coefficients. N M = N N/2 1.2 Non-Uniform Fast Fourier Transforms Equations 5, 6, and 7 refer to non-uniform transforms of types: (irregularly-sampled time-series data; equispaced frequency-domain data), (equispaced time-series data, discontinuous frequency-domain data), and (discontinuous time and frequency-domain data) which are equivalent to the transform: I II III 1.3 Inverse NFFTs Inverting A directly via a pseudo-inverse is computationally inefficient, relative to the standard set by an inverse Fourier transform in the equidistant case. Rather, it is more convenient to find a series of Fourier coefficients such that the original time series data is recoverable via the adjoint transform. This can be done in the least squares sense by minimising: A number of proposals to solve this optimisation problem, and others have been put forward, but the method varies depending on the condition of the inverse problem, especially for direct solutions [9]. Tables 1.3 and 1.3 are provided to clear any potential confusion, for transformations of types I & II. For the purposes of this work and to explicate the far more common scenario where irregularly sampled time-series data are provided, the focus will be exclusively on type-I transforms, although it would be relatively straightforward to apply the same thinking to type-II transforms. 1.4 Optimal interpolation problem The optimal interpolation problem has been solved earlier using the Conjugate Gradient Method that minimises the successive error at each iteration, rather than the residuals relative to the original data [10]. This work demonstrates the quadratic form of Equation 12, and offers a convenient method for solving the interpolative iNFFT without the need to iterate. In addition, it avoids problems with dimensionality by inverting the AAH matrix, which sacrifices time complexity for simplicity; this is an acceptable trade-off for small values of N and competitive with faster algorithms for the direct inversion of non-uniform fast Fourier transforms which require similar O(N2 ) matrix pre-computations [9]. can be used to define the Sobolev kernel, first as an intermediate and generalisable weight function: This paper is under CC 4.0 license. available on arxiv

Solving the Interpolative Inverse Non-Uniform Fast Fourier Transform Problem for Spectral Analysis

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 6. RESULTS 6.1. Cross-correlation search We cross-correlated the models described in Section 4 with the processed data and searched for peaks in the phase-folded cross-correlation function at the expected KP and vsys of GJ 486b. Figs. 2 − 8 show the results of our search for H2O, CO2, HCN, NH3, CH4, Na, and the 700 K solar abundance model with condensation, respectively. These figures show the 1D cross-correlation functions phase-folded using GJ 486b’s expected value of KP as well as its negative value. We show the result of using −KP as we later use it to inject model spectra into the data in Section 6.2. We find no evidence for robust correlations between any of our models[9] and the data. The most promising feature is of H2O in the IGRINS data set (see Fig. 2), which is at the 3σ level[10] in the expected position in KP and vsys. However, it is likely that this feature is produced by imperfectly removed water lines in the spectrum of GJ 486b’s M-dwarf host star, given that it is present after applying only one iteration of SYSREM, as shown in Fig. 9. We do not understand why these residuals were not removed by SYSREM and only appear in the IGRINS data set. However, we note that they appear to be qualitatively similar to those reported by Brogi et al. (2013). In their case, Brogi et al. (2013) detected molecular absorption in the dayside emission spectrum of 51 Pegasi b on two separate occasions, but not a third occasion when strong stellar residuals were present in the data. Chiavassa & Brogi (2019) revisited this data and used 3D simulations of stellar convection to remove the stellar spectrum. This method better suppressed the stellar residuals in the third observation and allowed the planetary signal to be detected. In a future study, it may be beneficial to investigate whether the method of Chiavassa & Brogi (2019) can better suppress the stellar residuals in our IGRINS data. Regarding our nondetection of Na, Caballero et al. (2022) suggest that if GJ 486b were to have a tenuous atmosphere with a surface pressure of 10−6 bar, nonthermal processes such as surface sputtering by ions may heat its atmosphere to temperatures on the order of 10,000 K. They show that a Na abundance of 10−9 in such an atmosphere would produce transit depths of approximately 1% in the cores of the Na D lines (5889.95 and 5895.92 ˚A), which could be detected by observing only a few transits with the Calar Alto high-Resolution search for M dwarfs with Exoearths with Near-infrared and optical Echelle Spectrographs (CARMENES; Quirrenbach et al. 2014). The spectral ranges of our observations do not include the Na D lines, but our IRD and SPIRou data sets do include the Na lines at 1.138 and 1.141 µm, which are much weaker and more difficult to detect. Therefore, while we found no evidence for a nonthermal Na atmosphere, we do not consider this to be evidence for its absence. We recommend further investigation of this scenario with additional observations targeting the Na D lines. 6.2. Constraining GJ 486b’s Atmospheric Composition As we did not detect the atmosphere of GJ 486b, here we determine the abundances that our data can rule out under the assumption that GJ 486b’s atmosphere is clear. We do this by injecting cloud-free model spectra into the reduced data before processing and crosscorrelating as in Sections 3 and 5. For each in-transit frame, we injected the model transmission spectrum at the velocity given by Equation 2 if KP were replaced by its negative value. We injected the signals at −KP so that they would not be affected by any weak real planet signals that may be present in the data. To optimally combine the different data sets, we summed the log-likelihoods. Our cross-correlation to log-likelihood mapping produced an array with dimensions KP, vsys, and α. Examples of the recovered conditional (2D) and marginalized (1D) likelihood distributions after injecting a water model into the data are shown in Fig. 10. We used the marginalized distributions of KP, vsys, and α (as shown in Fig. 10) to find their most likely values and their associated 1σ errors. With this approach, the significance to which an injected signal was recovered is well approximated by how well α is constrained (Gibson et al. 2020, 2022). This method works because we assume that marginalized distributions of α that are constant at α = 0, or unconstrained, correspond to a nondetection. Therefore, we are essentially measuring how significantly α differs from zero. However, in practice, our recovered values of α were slightly less than the true values, leading to a slight underestimate of the true recovered detection significances. This is because we did not explicitly account for how SYSREM distorts an underlying planet signal when removing the stellar and telluric lines. Gibson et al. (2022) showed that this can be corrected by pre-processing the models in an analogous way to how SYSREM processes the planet signal in the data. We investigated the feasibility of using this approach by trying it for a single model spectrum and a typical grid covering ±60 km s−1 around the expected KP and vsys with step sizes of 1 km s −1 . The run time of this trial on a reasonably capable workstation[11] was 80 minutes. Our full analysis uses about 640 different model spectra, which would therefore require a total run time of about 36 days. We consider this to be infeasible for this study, so we instead report our upper limits with the caveat that they likely underestimate the true upper limits. We were able to recover some injected models for H2O, CH4, NH3, HCN, CO2, and CO, indicating that our high-resolution observations can constrain the abundances of these chemical species under the assumption of GJ 486b having a clear atmosphere. As these species have different molecular weights, line strengths, and numbers of lines, they are each sensitive to different regions of VMR-MMW parameter space. These constraints are shown in Fig 11 for H2O, CH4, NH3, HCN, CO2, and CO, respectively. Each figure shows the VMRMMW constraints for the individual data sets (SPIRou, IRD, IGRINS) and all data sets combined. Models with higher MMWs have smaller atmospheric scale heights, making them more difficult to recover. Injected models that were recovered to significances of ≥5σ and 3≤σ<5 are colored in black and gray, respectively. Injected models that were recovered to significances of 0≤σ<3 are considered to have not been recovered and are shown in light blue. This means that we can rule out model atmospheres to 5σ and 3σ in the VMR-MMW regions colored black and gray, respectively. Conversely, the light blue regions represent atmospheric compositions that are allowed by our data. We obtain strong constraints on the presence of H2O in GJ 486b’s atmosphere. Assuming a clear atmosphere, our observations rule out log10(VMR) ≥ −3 for atmospheric MMWs ≤ 5 and log10(VMR) ≥ −4 for MMWs ≤2.5 to 5σ. Therefore, our observations are inconsistent with a clear H2/He-dominated atmosphere with solar (or somewhat subsolar) H2O abundances. We can also, less confidently, rule out a clear atmosphere of log10(VMR) ≥ −2 for MMWs ≤ 16 and a clear 100% H2O atmosphere to 3σ. Our results, therefore, suggest that GJ 486b does not have a clear H2O-dominated atmosphere. This could have important implications for planetary interior models, which often predict H2O-dominated atmospheres as the most likely possibility for 3 M⊕ planets like GJ 486b (e.g., Ortenzi et al. 2020). Our derived constraints on the abundances of CH4, NH3, HCN, CO2, and CO are summarized in Table 2. These constraints show that our observations are inconsistent with a clear H2/He-dominated atmosphere with a solar CH4 abundance. However, solar abundances of NH3, HCN, CO2, and CO are consistent with our data. For cloud-free models including all the above species at solar abundances, we rule out to 5σ those with temperatures > 500 K, as shown in Fig. 12. We were not able to recover any of our injected models for C2H2, FeH, H2S, Na, K, PH3, SiO, TiO, or VO. This indicates that, regardless of their VMR or the background atmosphere MMW, our observations are not sensitive to these chemical species. For all the species considered here, the data from IGRINS on Gemini-S offers the strongest limits. This likely results from Gemini-S having a larger collecting area than SPIRou’s CFHT and the wavelength coverage (1.45-2.45 µm) of IGRINS extending further into the infrared than that of IRD (0.97-1.75 µm). Overall, these upper limits indicate that GJ 486b lacks a clear H/He-dominated atmosphere with solar abundances. However, some possible cloudy atmospheres or high-MMW secondary atmospheres are allowed by our data. [9] Including both the Exomol (Yurchenko & Tennyson 2014) and HITEMP 2020 (Hargreaves et al. 2020) opacity data for CH4. [10] Approximated as the amplitude of the cross-correlation function at the expected velocity divided by the standard deviation of the surrounding regions. [11] With the following specifications: Ryzen 5900X (12 core) CPU, 128 GB of DDR 3200 RAM, and a PCIe 4.0 M.2 SSD.

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Results

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References ABSTRACT Terrestrial exoplanets orbiting M-dwarf stars are promising targets for transmission spectroscopy with existing or near-future instrumentation. The atmospheric composition of such rocky planets remains an open question, especially given the high X-ray and ultraviolet flux from their host M dwarfs that can drive atmospheric escape. The 1.3 R⊕ exoplanet GJ 486b (Teq ∼ 700 K), orbiting an M3.5 star, is expected to have one of the strongest transmission spectroscopy signals among known terrestrial exoplanets. We observed three transits of GJ 486b using three different high-resolution spectrographs: IRD on Subaru, IGRINS on Gemini-South, and SPIRou on the Canada-France-Hawai’i Telescope. We searched for atmospheric absorption from a wide variety of molecular species via the cross-correlation method, but did not detect any robust atmospheric signals. Nevertheless, our observations are sufficiently sensitive to rule out several clear atmospheric scenarios via injection and recovery tests, and extend comparative exoplanetology into the terrestrial regime. Our results suggest that GJ 486b does not possess a clear H2/He-dominated atmosphere, nor a clear 100% water-vapor atmosphere. Other secondary atmospheres with high mean molecular weights or H2/He-dominated atmospheres with clouds remain possible. Our findings provide further evidence suggesting that terrestrial planets orbiting M-dwarf stars may experience significant atmospheric loss. : Exoplanet atmospheres (487) — Planetary atmospheres (1244) — Exoplanets (498) — Exoplanet atmospheric composition (2021) Keywords 1. INTRODUCTION Exoplanets that transit M-dwarf stars produce relatively deep transits due to their high planet-star radius ratios. Consequently, it may be possible to detect biosignatures in the atmospheres of terrestrial exoplanets with existing or near-future instrumentation (e.g., Snellen et al. 2013; Wunderlich et al. 2019). However, the long-term habitability of such planets depends crucially on the stability of their atmospheres, which are subject to high X-ray + extreme UV (XUV) emissions during the pre-main-sequence phase of their M-dwarf hosts (e.g., Shkolnik & Barman 2014; Peacock et al. 2020) as well as flare events (e.g., Neves Ribeiro do Amaral et al. 2022) that can drive atmospheric escape (e.g., Vidal-Madjar et al. 2003; Bourrier et al. 2017a; Airapetian et al. 2017). Terrestrial exoplanets are expected to exhibit a rich diversity in atmospheric compositions. Exoplanets with Rp < 1.4 R⊕ and orbital periods < 100 days likely form as gas-rich sub-Neptunes that subsequently lose their primordial atmospheres due to photoevaporation (Rogers & Owen 2021). The amount of primordial atmosphere retained depends on several factors, such as their host star’s spectral energy distribution and activity levels, along with how much gas they accrete during formation (e.g., Bolmont et al. 2017; Owen et al. 2020). Should the primordial hydrogen-dominated atmosphere be lost, a secondary atmosphere may be retained. The composition of secondary atmospheres depends on a variety of processes, including interior outgassing, impact delivery, erosion, atmosphere-surface exchange, weathering, and volatile sequestration (e.g., Rogers et al. 2011). Driven by these processes, hot terrestrial planets (T & 1500 K) are predicted to have atmospheres containing atoms and ions, such as Na or Ca+, produced by surface vaporization (Schaefer & Fegley 2009) — though other hydrogen- or nitrogen-rich compositions are possible (Miguel 2019). However, cooler terrestrial planets are likely rich in molecular species, such as H2O, CO2, CO, O2, C2H2, and CH4 (e.g., Ramirez & Kaltenegger 2014; Madhusudhan et al. 2016; Kite & Schaefer 2021). Given the range of theoretical possibilities, observational constraints on the composition of terrestrial exoplanet atmospheres are crucially needed. While there have been several attempts to detect the atmospheres of a few terrestrial exoplanets, these have only yielded constraints from nondetections. For example, 55 Cancri e is a 1.9 R⊕ exoplanet with a temperature of approximately 2400 K due to its 17 hr orbit around its G8V host star (Morris et al. 2021). Hubble Space Telescope observations suggest that 55 Cancri e may possess a thick atmosphere (Tsiaras et al. 2016). However, phase curve observations with the Spitzer space telescope (Demory et al. 2016) and ground-based high-resolution transmission spectroscopy point toward 55 Cancri e not possessing a thick atmosphere or having no atmosphere at all (Ridden-Harper et al. 2016; Esteves et al. 2017; Jindal et al. 2020; Deibert et al. 2021a; Zhang et al. 2021). A similar result was found for the cooler 1.3 R⊕ exoplanet LHS 3844 b, which has an equilibrium temperature of 800 K and orbits an M5 host star (Vanderspek et al. 2019). Kreidberg et al. (2019) observed the phase curve of LHS 3844 b with the Spitzer space telescope and found that thick atmospheres with surface pressures greater than 10 bar could be ruled out, while less massive atmospheres would be susceptible to erosion by the stellar wind. Additionally, they found that a bare rock model fit their data well. Another candidate for atmospheric characterization that orbits an M-dwarf is GJ 1132b, which has a radius, mass, and equilibrium temperature of 1.1 R⊕, 1.7 M⊕, and 580 K, respectively (Berta-Thompson et al. 2015). Using transit observations from the Hubble Space Telescope (HST), Swain et al. (2021) inferred that GJ 1132b possesses a H/He-rich atmosphere with large-amplitude spectral features. However, using the same HST data, Libby-Roberts et al. (2022) and Mugnai et al. (2021) independently found a featureless transmission spectrum, possibly indicating an atmosphere with a high mean molecular weight (MMW), a high-altitude aerosol layer, or a near-total lack of an atmosphere. These contrasting results highlight the difficulty of characterizing the atmospheres of small planets, even with space-based instrumentation. Comprehensive searches for the atmospheres of the seven approximately Earth-sized planets that transit the M8V star TRAPPIST-1 have also been carried out. These transit observations with the Hubble and Spitzer space telescopes all point toward the absence of extended hydrogen-dominated atmospheres. However, denser, potentially habitable atmospheres are consistent with the observational limits for some TRAPPIST-1 planets (de Wit et al. 2016; Bourrier et al. 2017b,a; de Wit et al. 2018; Zhang et al. 2018; Wakeford et al. 2019; Gressier et al. 2022). GJ 486b[1] is a 1.3 R⊕ and 2.8 M⊕ exoplanet with an equilibrium temperature of about 700 K, an M3.5 V host star of J-band magnitude 7.2, and an orbital period of 1.5 days (Trifonov et al. 2021). These system properties make GJ 486b well suited to atmospheric characterization with transmission spectroscopy, as reflected by it having one of the largest transmission spectroscopy metrics (as defined by Kempton et al. 2018) of all known terrestrial planets (Trifonov et al. 2021). Therefore, GJ 486b is an ideal observational target to inform our understanding of terrestrial planets around M-dwarf stars. Previous studies have surmised that a wide range of atmospheric compositions are possible for GJ 486b. Caballero et al. (2022) refined GJ 486b’s mass and radius, and presented a suite of interior and atmospheric models. They showed that possible atmospheres include: (i) a H/He-dominated atmosphere with solar abundances; (ii) a H/He atmosphere with enhanced metallicity (e.g., 100x solar abundances); (iii) a H2O-dominated atmosphere; (iv) a CO2-dominated atmosphere; or (v) bare rock with a tenuous atmosphere, possibly containing Na. Geochemical models based on initial conditions representative of a range of possible planets suggest that a H2O-dominated atmosphere is the most likely outcome for a 3 M⊕ planet like GJ 486b (Ortenzi et al. 2020). However, carbonaceous chondrite outgassing measurements suggest that CO2-dominated atmospheres could also evolve on terrestrial planets (Thompson et al. 2021). Differentiating between these scenarios requires spectroscopic observations. Molecular species such as those expected to be in GJ 486b’s atmosphere produce dense forests of spectral lines that can be individually resolved with high-resolution spectroscopy. The signals from these lines can be combined, even if they are individually hidden in the noise, by cross-correlating the data with a template containing the molecular lines (e.g., Snellen et al. 2010; Birkby 2018). This powerful technique has facilitated the detection of species such as H2O, CO, HCN, CH4, NH3, and C2H2 in the transmission spectra of hot Jupiters (e.g., Giacobbe et al. 2021). As high-resolution spectroscopy probes line cores, which contain information about the upper (low-pressure) regions of exoplanet atmospheres, it is highly complementary to low-resolution observations from telescopes such as the JWST, which probe higher pressures (Brogi & Line 2019). JWST is scheduled to observe two transits and eclipses of GJ 486b in programs GO 1981[2] (P.I.: K. Stevenson) and GO 1743[3] (P. I.: M. Mansfield), respectively. Here, we present the first high-resolution transmission spectroscopy observations of GJ 486b. We use three transits to constrain GJ 486b’s atmospheric composition. In what follows, in Sections 2 and 3 we describe our observations and data reduction approach. Section 4 describes our model spectra, while Section 5 describes our cross-correlation methodology. Section 6 presents our results, while Section 8 discusses the implications of our results for the upcoming JWST observations. Finally, Section 9 offers concluding remarks. [1] Alternative names include Wolf 437b and TOI 1827b. [2] program-information.html?id=1981 https://www.stsci.edu/jwst/science-execution/ [3] program-information.html?id=1743 https://www.stsci.edu/jwst/science-execution/

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Abstract & Intro

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 3. DATA REDUCTION AND PROCESSING The IRD transit data were reduced following the approach of Hirano et al. (2020), which uses a custom code to correct for a known count bias that depends on the readout channel (Kuzuhara et al. 2018). Subsequent reduction steps were performed using the echelle package of IRAF. As these observations did not use the laser frequency comb, the wavelength calibration was based on the Th-Ar lamp. The wavelength solution was stable to within approximately 0.04 km s−1 over the course of the observations. This stability was determined by first cross-correlating every frame with a model telluric spectrum produced using SkyCalc (Noll et al. 2012; Jones et al. 2013) to find the telluric radial velocity of each frame. We then calculated the standard deviation of all telluric radial velocities during the night (after applying a barycentric correction). The model produced by SkyCalc is highly customizable, but the most important parameters are target elevation or airmass and the precipitable water vapor. The IGRINS transit data were reduced by the IGRINS Pipeline Package (PLP; Sim et al. 2014; Mace et al. 2018). Though this pipeline provides wavelengthcalibrated data, the PLP wavelength calibration drifted by about 1 kms−1 through the course of the observations. To fit and correct this drift, we first crosscorrelated SkyCalc telluric models (as above) with every frame in spectral orders that had no saturated telluric lines. The spectral orders used for these crosscorrelations are shown in Table 3. For each frame, we then summed the cross-correlation functions (CCFs) from all our considered orders to produce a master CCF. Next, we fit a Gaussian to the master CCF of each frame and adopted the Gaussian center as the radial velocity of the observed telluric spectrum. Finally, we fit the telluric radial velocities with a third-degree polynomial as a function of time and used this to shift the data onto a common wavelength grid using linear interpolation. The amplitudes of the corrective shifts were approximately 0.5−1 km s−1 . The SPIRou transit data were reduced by the observatory using the SPIRou Data Reduction Software (DRS) [4] . This pipeline provides data with and without a telluric correction, and we opted to use the telluric-corrected data. For all data sets, after the above procedures, we normalized the spectra on a per-order basis to have a continuum flux of 1. We then masked all spectral regions with a normalized flux less than 0.3. For the IGRINS data, we also masked telluric emission lines, as in Rousselot et al. (2000) and Oliva et al. (2015). We then applied the SYSREM algorithm (Tamuz et al. 2005) to all data sets to remove remaining telluric features and other systematic trends. We applied SYSREM to our data in fluxes (without first converting to magnitudes), as in Gibson et al. (2020) and Nugroho et al. (2021). Because SYSREM incorporates uncertainties, we estimated the uncertainties of the normalized data as where σt and σλ are the standard deviations in the time and wavelength dimensions, respectively, σt,λ is the standard deviation in both the time and wavelength dimensions, and ⊗ is the outer product operator. To determine an effective number of SYSREM iterations to use, we followed a well-established approach and examined how each order’s variance decreased as a function of the number of applied SYSREM iterations (e.g., Deibert et al. 2021a; Herman et al. 2022). For each order, we examined plots of the variance as a function of the number of SYSREM iterations and identified by eye the number of iterations where the decrease in data variance began to plateau. We noted the number of SYSREM iterations determined for each order and then used the most common number of iterations as a constant for each data set. This resulted in us applying five SYSREM iterations to all orders of the SPIRou and IRD data and 10 SYSREM iterations to all orders of the IGRINS data. While our chosen values are likely not the formal optimal values, they are a close approximation, as telluric features and other systematic trends were removed effectively. In general, previous studies have found that the exact number of SYSREM iterations has little to no effect on the final results (e.g., Deibert et al. 2021b). Some IGRINS orders, as indicated in Table 3, had near-total telluric extinction across most of their spectral range. Such severe contamination cannot be corrected by SYSREM, so we excluded these orders from the analysis. Finally, for all data sets, we removed any outlying points more than three standard deviations away from the mean. Barycentric corrections for the IRD and IGRINS data were calculated with the radial velocity correction function in Astropy. The same corrections for the SPIRou data were provided by the DRS. [4] http://www.cfht.hawaii.edu/Instruments/SPIRou/SPIRou pipeline.php

High-Resolution Transmission Spectroscopy: Data Reduction and Processing

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 2. OBSERVATIONS We used three different instruments to observe three separate transit events of GJ 486b. The first transit was observed with the Infrared Doppler (IRD) instrument on the 8.2 m Subaru telescope, providing a wavelength coverage of 0.97-1.75 µm at a resolving power of R = 70k (Tamura et al. 2012; Kotani et al. 2018). The second transit was observed with the Immersion GRating INfrared Spectrometer (IGRINS) on the 8.1 m GeminiSouth telescope (program ID: GS-2021A-DD-105), providing a wavelength coverage of 1.45-2.45 µm at a resolving power of R = 45k (Park et al. 2014; Lee & Gullikson 2016; Mace et al. 2018). The third transit was observed with the SPectropolarim`etre InfraROUge (SPIRou) on the 3.6 m Canada-France-Hawai’i Telescope (CFHT) in queue mode (run ID: 21BC34), providing a wavelength coverage of 0.98-2.35 µm at a resolving power of R = 75k (Artigau et al. 2014). The observation start time, duration, integration time, cadence, total number of frames, number of frames in transit, number of frames out of transit, and average signal-to-noise (S/N) ratio per pixel for these observations are shown in Table 1.

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Observation

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 4. MODEL SPECTRA We searched for chemical species in GJ 486b’s atmosphere through a cross-correlation analysis with model transmission spectra. We generated a grid of template spectra using the open-source radiative transfer code petitRADTRANS[5] (Molli`ere et al. 2019). Each template contains one or more absorbing chemical species embedded in a background of spectrally inactive gas. Specifically, we searched for a wide range of gaseous absorbers: C2H2, CH4, CO, CO2, FeH, H2O, HCN, H2S, K, Na, NH3, PH3, SiO, TiO, and VO. We used the default opacity data included with petitRADTRANS (see Molli`ere et al. 2019) for all species. However, for CH4 we used both the default opacity data from Exomol (Yurchenko & Tennyson 2014) and our custom-made opacity grid based on a line list adopted from HITEMP 2020 (Hargreaves et al. 2020). We produced the custom opacity grid following the pressure-temperature grid in the petitRADTRANS website (e.g., PTgrid.dat). We calculated the cross section using HELIOS-K 2.0 (Grimm & Heng 2015; Grimm et al. 2021) with a line wing cutoff of 100 cm−1 at a resolution of 0.001 cm−1 which was later resampled at a constant resolution of R = 106 . Our templates include two chemical models: (i) single chemical species models and (ii) multispecies chemical equilibrium models. Our single-species models span a range of volume mixing ratios (VMRs) and MMWs. The VMRs range from log10(VMR) = −7 to 0 (assumed constant with altitude). We varied the MMWs independently of the VMRs, subject to the following boundary conditions: (i) the minimum MMW corresponds to a H2-dominated atmosphere with the template species (i.e. MMWmin ≈ 2 · (1 − VMRi) + VMRi · MMWi); and (ii) the maximum MMW corresponds to an N2-dominated atmosphere with the template species (i.e. MMWmax ≈ 28 · (1 − VMRi) + VMRi · MMWi). These boundary conditions ensure the MMWs are physically consistent with the presence of a spectrally inactive gas mixture with unknown ratios of H2, He, or N2. These models assumed an isothermal pressure-temperature (P-T) profile at GJ 486b’s equilibrium temperature: 700 K (Trifonov et al. 2021). Our second set of models included all the above mentioned spectrally active species with thermochemical equilibrium abundances. We computed equilibrium abundances for a solar composition atmosphere using the open-source code GGchem[6] (Woitke et al. 2018), assuming isothermal P-T profiles at 400, 500, 600, and 700 K. We considered three different approaches for modeling condensation in GGchem: (1) no condensation, (2) condensation included, and (3) condensation included, but condensates removed (e.g., rain out). These abundance profiles are shown in Fig. 1. Although condensation would likely result in the production of aerosols such as clouds or hazes, we do not attempt to account for the effect of such aerosols on the resulting transmission spectrum. Our petitRADTRANS models were computed line by line at R = 106 using 130 layers spaced evenly in logpressure from 10−11 to 102 bar. We then resampled the model spectra to the resolution of the data (R = 45 k, 70 k, and 75 k for IGRINS, IRD, and SPIRou, respectively) using the SpectRes package[7] (Carnall 2017) which preserves the integrated flux. Because cross-correlations are not sensitive to continua, we subtracted the continuum from our model spectra (after binning to the data resolution) to produce our final cross-correlation templates. [5] https://petitradtrans.readthedocs.io/en/latest/ [6] https://github.com/pw31/GGchem [7] https://spectres.readthedocs.io/en/latest/

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Model Spectra

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 5. CROSS-CORRELATION METHOD To search for GJ 486b’s atmosphere, we crosscorrelated the models described in Section 4 with the processed data from Section 3. We cross-correlated each model with one processed frame at a time, to produce a cross-correlation function (CCF). We phase-folded these CCFs by shifting them into the planet rest frame. GJ 486b’s radial velocity as a function of time, relative to its host star, is given by To optimally combine our three data sets, we mapped our cross-correlation functions to log-likelihoods (following, e.g., Brogi & Line 2019; Gibson et al. 2020; Herman et al. 2022). This approach allows the log-likelihoods from each data set to be trivially summed. The log-likelihood for each data set, ln LD, is calculated according to where the D subscript refers to the data set (e.g., IRD, IGRINS, and SPIRou), N is the total number of intransit data points in the data set, and χ 2 is related to the cross-correlation function, CCF, via where fi is the mean-subtracted spectrum, σi is the outer product of the standard deviation of each wavelength and exposure bin (normalized by the standard deviation of the spectra in each order), mi is the meansubtracted Doppler-shifted model, α is a scaling factor to allow uncertainty in the scale of the model, and the CCF is defined as We determined which frames were in transit by generating model transit light curves with the BAsic Transit Model cAlculatioN in Python (BATMAN[8] ) package (Kreidberg 2015), using the system parameters reported by Trifonov et al. (2021), and weighted all in-transit frames equally. We converted the summed log-likelihood to a normalized likelihood, Lnorm, according to Lnorm = exp(lnL − max(lnL)). [8] https://github.com/lkreidberg/batman

High-Resolution Transmission Spectroscopy: Cross-Correlation Method

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of LinksTable of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 7. CLOUDS AND HAZES Clouds and hazes may be present in a wide range of exoplanet atmospheres (e.g., Helling 2019) and tend to suppress spectral features in exoplanet transmission spectra (e.g., Sing et al. 2016; Gandhi et al. 2020). As our models did not account for clouds (see Section 4), the constraints presented in Section 6.2 are only valid under the assumption that GJ 486b’s atmosphere is clear (i.e., free of clouds and haze). To assess how our results would change without this assumption, we examined the effect of adding gray cloud decks at varying pressure levels to our hydrogendominated model with water at solar abundances (see Section 4). These models have cloud decks at pressures of 10−6 bar to 10−2 bar with factors of 10 between steps. As all the models used in this study set the planet radius to correspond to a pressure of 10−2 bar, the model with a cloud deck at this level is essentially cloud-free. These models are shown before and after continuum subtraction in the top left and right panels of Fig. 13. We find that lower cloud deck pressures (corresponding to higher altitudes) result in more suppression of the spectral lines. To quantify the level of suppression caused by the clouds, we calculated auto-cross-correlation functions for each model. The lower left and right panels of Fig. 13 show the auto-cross-correlation functions and peak auto-cross-correlation amplitude, respectively, after normalizing so that the maximum value is 1. By comparing to the reference model with a cloud deck at 10−2 bar (essentially cloud-free), we find that cloud decks at pressures of .10−3 bar reduce the strength of the cross-correlation signal by a factor of &3. This implies that, if an injection/recovery test were performed (as in Section 6.2) with a cloudy model, it would be recovered to a lower significance than the same model without clouds. Therefore, some of the VMR-MMW parameter space that was ruled out by our data while assuming a clear atmosphere will still be allowed for a cloudy atmosphere.

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Clouds and Hazes

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of LinksTable of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 8. IMPLICATIONS FOR JWST GJ 486b will be observed by JWST in both transit and eclipse. Two transits of GJ 486b are scheduled to be observed with JWST’s Near-InfraRed Spectrograph (NIRSpec), with the G395H grism in Bright Object Time Series (BOTS) mode, as part of a larger program to search for atmospheres on planets transiting M-dwarf stars (GO 1981[12], P.I.: K. Stevenson). Two eclipses with the Mid-Infrared Instrument (MIRI) will further probe GJ 486b’s dayside atmosphere (GO 1743[13], P. I.: M. Mansfield). Our constraints on GJ 486b’s atmospheric composition (Section 6.2) can inform these future JWST observations. Caballero et al. (2022) show that a H2/Hedominated atmosphere can be readily detected by JWST with two transits. However, we can confidently rule out to 5σ a clear H2/He-dominated atmosphere with H2O, CH4, NH3, HCN, CO2, and CO at solar abundances. We further investigated the prospects for JWST to detect GJ 486b’s atmosphere via simulated observations. We used PandExo (Batalha et al. 2017) to simulate two transits of GJ 486b with NIRSpec G395H. To match the scheduled observations, we used an observation duration of 5.14 hr, the R = 2700 f290lp grism, and the S1600A1 SUB2048 subarray option. We set the number of groups per integration to the ‘optimized’ default, the saturation limit to 80% of the full well capacity, and assumed no noise floor. We considered eight cloud-free model atmospheres compatible with our high-resolution observations. We focused on H2O and CO2 as they are the main expected absorbers over the NIRSpec G395H spectral range. The models also contained background H2. Four of the models vary the H2O abundance and atmospheric mean molecular weight while CO2 is fixed at a solar abundance (VMR ,CO2 ≈ 5×10−9 ). The other four models vary the CO2 abundance and the atmospheric mean molecular weight while H2O is fixed to a solar abundance (VMR ,H2O ≈ 5×10−4 ). The models with variable H2O have log10(VMRH2O) = −7, −5, −4 and −2, and MMWs of 2, 5, 10, and 18, respectively. The models with variable CO2 have log10(VMRCO2) = −7, −5, −4 and −1, and MMWs of 2, 3, 7, and 7.5, respectively. These values of VMR and MMW were chosen because they lie just outside the region of VMR-MMW parameter space ruled out by our high-resolution observations in Fig 11. The simulated JWST observations for these models are shown in Fig. 14. By comparing the simulated observation error bars to the size of the features in the model spectra, it is apparent that the scheduled transit observations could readily distinguish all considered models with variable CO2 abundance and a solar H2O abundance (lower four panels in Fig. 14). However, these observations may not be able to distinguish our considered models with variable H2O abundance and a solar CO2 abundance (upper four panels in Fig. 14). This indicates the complementary capabilities of high-resolution ground-based and JWST observations, because the former has greater sensitivity to H2O while the latter has greater sensitivity to CO2. We note that our atmospheric models are unable to make predictions that could inform the eclipse observations — since they have isothermal pressuretemperature profiles, which produce featureless emission spectra. Future investigations that also consider a range of possible pressure-temperature profiles may provide useful insights into GJ 486b’s emission spectrum. ive than JWST to oxygen in a terrestrial exoplanet’s atmosphere (Snellen et al. 2013). In this regime of high signal-to-noise ratios for terrestrial exoplanets, high-resolution spectroscopy’s unique capabilities will allow detections of Doppler shifts (which can indicate atmospheric winds; Snellen et al. 2010), line broadening (which can indicate rotation; Snellen et al. 2014) and potentially molecular isotopologues (Molli`ere & Snellen 2019). [12] https://www.stsci.edu/jwst/science-execution/ program-information.html?id=1981 [13] https://www.stsci.edu/jwst/science-execution/ program-information.html?id=1743

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Implications for JWS

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References 9. CONCLUSION In this study, we searched for the atmosphere of the 1.3 R⊕ exoplanet GJ 486b using high-resolution transmission spectroscopy. Our resultant atmospheric constraints additionally offer an improved understanding of the possible atmospheres for terrestrial planets orbiting M-dwarf stars. We observed three transits of GJ 486b with the spectrographs IRD, IGRINS, and SPIRou, and searched for absorption by its atmosphere with the high-resolution cross-correlation method. We searched for C2H2, CH4, CO, CO2, FeH, H2O, HCN, H2S, K, Na, NH3, PH3, SiO, TiO, and VO, but did not detect any robust atmospheric signals. Our best potential detection was H2O at the 3σ level, seen only in the IGRINS data set. However, this potential signal is likely an artifact of imperfectly removed H2O lines in the stellar spectrum. We suggest that it may be possible to improve the suppression of these stellar lines by using 3D models of stellar convection, as shown by Chiavassa & Brogi (2019). Nevertheless, we derived informative upper limits on the abundances of many chemical species by performing signal injection and recovery tests. Our recovery tests allow us to rule out a clear H2/He-dominated atmosphere with solar molecular abundances to a confidence of >5σ, while also ruling out a clear 100% water atmosphere to a confidence of 3σ. Since a water-dominated atmosphere is often considered to be the most likely possibility for 3 M⊕ planets like GJ 486b (e.g., Ortenzi et al. 2020), our results could have important implications for planetary interior models. We also investigated the implications of our findings for the upcoming JWST transit observations of GJ 486b. We found that these observations will be especially sensitive to the presence of CO2, but with weaker sensitivity to H2O, highlighting the complementary capabilities of high-resolution and JWST observations. In summary, our results indicate that GJ 486b does not possess a cloud-free H/He-dominated atmosphere that has solar abundances. However, it may possess a secondary atmosphere with high mean molecular weight or a H2/He-dominated atmosphere with clouds. Our findings provide further evidence suggesting that terrestrial planets orbiting M-dwarf stars experience significant atmospheric loss. ACKNOWLEDGMENTS We thank the anonymous reviewer for constructive comments. We are also grateful to Hajime Kawahara for helpful suggestions. This research is based [in part] on data collected at the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan. We are honored and grateful for the opportunity of observing the Universe from Maunakea, which has cultural, historical, and natural significance in Hawaii. This work used the Immersion Grating Infrared Spectrometer (IGRINS) that was developed under a collaboration between the University of Texas at Austin and the Korea Astronomy and Space Science Institute (KASI) with the financial support of the Mt. Cuba Astronomical Foundation, of the US National Science Foundation under grants AST-1229522 and AST-1702267, of the McDonald Observatory of the University of Texas at Austin, of the Korean GMT Project of KASI, and of the Gemini Observatory. Based on observations obtained at the international Gemini Observatory, a program of NSF’s NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation on behalf of the Gemini Observatory partnership: the National Science Foundation (United States), the National Research Council (Canada), Agencia Nacional de Investigaci´on y Desarrollo (Chile), Ministerio de Ciencia, Tecnolog´ıa e Innovaci´on (Argentina), Minist´erio da Ciˆencia, Tecnologia, Inova¸c˜oes e Comunica¸c˜oes (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea). Based on observations obtained at the CanadaFrance-Hawaii Telescope (CFHT), which is operated from the summit of Maunakea by the National Research Council of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique of France, and the University of Hawaii. The observations at the Canada-France-Hawaii Telescope were performed with care and respect from the summit of Maunakea which is a significant cultural and historic site. Based on observations obtained with SPIRou, an international project led by Institut de Recherche en Astrophysique et Plan´etologie, Toulouse, France. This research has made use of the Extrasolar Planet Encyclopaedia, NASA’s Astrophysics Data System Bibliographic Services, and the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. M.T. is supported by JSPS KAKENHI grant Nos. 18H05442, 15H02063, and 22000005. S. K. N. is supported by JSPS KAKENHI grant No. 22K14092. J.D.T’s support for this work was provided by NASA through the NASA Hubble Fellowship grant #HSTHF2-51495.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5- 26555. : 8.2 m Subaru telescope (IRD), 8.1 m Gemini-South telescope (IGRINS), 3.6 m Canada France Hawai’i Telescope (SPIRou) Facilities : Astropy (Astropy Collaboration et al. 2013, 2018); SciPy (Virtanen et al. 2020); NumPy (Harris et al. 2020); petitRADTRANS (Molli`ere et al. 2019); SpectRes (Carnall 2017). Software

High-Resolution Transmission Spectroscopy: Conclusion and Acknowledgement

This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrew Ridden-Harper, Department of Astronomy and Carl Sagan Institute, Cornell University & Las Cumbres Observatory; (2) Stevanus K. Nugroho, Astrobiology Center & Japan & National Astronomical Observatory of Japan; (3) Laura Flagg, Department of Astronomy and Carl Sagan Institute, Cornell University; (4) Ray Jayawardhana, Department of Astronomy, Cornell University; (5) Jake D. Turner, Department of Astronomy and Carl Sagan Institute, Cornell University & NHFP Sagan Fellow; (6) Ernst de Mooij, Astrophysics Research Centre, School of Mathematics and Physics & Queen’s University Belfast; (7) Ryan MacDonald, Department of Astronomy and Carl Sagan Institute; (8) Emily Deibert, David A. Dunlap Department of Astronomy & Astrophysics, University of Toronto & Gemini Observatory, NSF’s NOIRLab; (9) Motohide Tamura, Dunlap Institute for Astronomy & Astrophysics, University of Toronto (10) Takayuki Kotani, Department of Astronomy, Graduate School of Science, The University of Tokyo, Astrobiology Center & National Astronomical Observatory of Japan; (11) Teruyuki Hirano, Astrobiology Center, National Astronomical Observatory of Japan & Department of Astronomical Science, The Graduate University for Advanced Studies; (12) Masayuki Kuzuhara, Las Cumbres Observatory & Astrobiology Center; (13) Masashi Omiya, Las Cumbres Observatory & Astrobiology Center; (14) Nobuhiko Kusakabe, Las Cumbres Observatory & Astrobiology Center. Table of Links Abstract & Introduction Observation Data Reduction and Processing Model Spectra Cross-Correlation Method Results Clouds and Hazes Implications for JWST Conclusion and Acknowledgement Appendix & References APPENDIX A. EXCLUDED WAVELENGTH REGIONS Here, we explicitly define the wavelength regions that were excluded from our analysis for having severe telluric contamination or insufficient signal-to-noise ratios (especially at the edges of the spectral orders). The excluded regions for the IGRINS data are shown in Table 3. REFERENCES Airapetian, V. S., Glocer, A., Khazanov, G. V., et al. 2017, ApJL, 836, L3, doi: 10.3847/2041-8213/836/1/L3 Artigau, E., Kouach, D., Donati, J.-F., et al. 2014, in ´ Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 914715, doi: 10.1117/12.2055663 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Batalha, N. E., Mandell, A., Pontoppidan, K., et al. 2017, PASP, 129, 064501, doi: 10.1088/1538-3873/aa65b0 Berta-Thompson, Z. K., Irwin, J., Charbonneau, D., et al. 2015, Nature, 527, 204, doi: 10.1038/nature15762 Birkby, J. L. 2018, arXiv e-prints, arXiv:1806.04617. https://arxiv.org/abs/1806.04617 Bolmont, E., Selsis, F., Owen, J. E., et al. 2017, MNRAS, 464, 3728, doi: 10.1093/mnras/stw2578 Bourrier, V., de Wit, J., Bolmont, E., et al. 2017a, AJ, 154, 121, doi: 10.3847/1538-3881/aa859c Bourrier, V., Ehrenreich, D., Wheatley, P. J., et al. 2017b, A&A, 599, L3, doi: 10.1051/0004-6361/201630238 Brogi, M., Line, M., Bean, J., D´esert, J. M., & Schwarz, H. 2017, ApJL, 839, L2, doi: 10.3847/2041-8213/aa6933 Brogi, M., & Line, M. R. 2019, AJ, 157, 114, doi: 10.3847/1538-3881/aaffd3 Brogi, M., Snellen, I. A. G., de Kok, R. J., et al. 2013, ApJ, 767, 27, doi: 10.1088/0004-637X/767/1/27 Caballero, J. A., Gonzalez-Alvarez, E., Brady, M., et al. 2022, arXiv e-prints, arXiv:2206.09990. https://arxiv.org/abs/2206.09990 Carnall, A. C. 2017, arXiv e-prints, arXiv:1705.05165. https://arxiv.org/abs/1705.05165 Chiavassa, A., & Brogi, M. 2019, A&A, 631, A100, doi: 10.1051/0004-6361/201936566 de Wit, J., Wakeford, H. R., Gillon, M., et al. 2016, Nature, 537, 69, doi: 10.1038/nature18641 de Wit, J., Wakeford, H. R., Lewis, N. K., et al. 2018, Nature Astronomy, 2, 214, doi: 10.1038/s41550-017-0374-z Deibert, E. K., de Mooij, E. J. W., Jayawardhana, R., et al. 2021a, AJ, 161, 209, doi: 10.3847/1538-3881/abe768 —. 2021b, ApJL, 919, L15, doi: 10.3847/2041-8213/ac2513 Demory, B.-O., Gillon, M., de Wit, J., et al. 2016, Nature, 532, 207, doi: 10.1038/nature17169 Esteves, L. J., de Mooij, E. J. W., Jayawardhana, R., Watson, C., & de Kok, R. 2017, AJ, 153, 268, doi: 10.3847/1538-3881/aa7133 Gandhi, S., Brogi, M., & Webb, R. K. 2020, MNRAS, 498, 194, doi: 10.1093/mnras/staa2424 Giacobbe, P., Brogi, M., Gandhi, S., et al. 2021, Nature, 592, 205, doi: 10.1038/s41586-021-03381-x Gibson, N. P., Nugroho, S. K., Lothringer, J., Maguire, C., & Sing, D. K. 2022, MNRAS, 512, 4618, doi: 10.1093/mnras/stac091 Gibson, N. P., Merritt, S., Nugroho, S. K., et al. 2020, MNRAS, 493, 2215, doi: 10.1093/mnras/staa228 Gressier, A., Mori, M., Changeat, Q., et al. 2022, A&A, 658, A133, doi: 10.1051/0004-6361/202142140 Grimm, S. L., & Heng, K. 2015, The Astrophysical Journal, 808, 182, doi: 10.1088/0004-637X/808/2/182 Grimm, S. L., Malik, M., Kitzmann, D., et al. 2021, ApJS, 253, 30, doi: 10.3847/1538-4365/abd773 Hargreaves, R. J., Gordon, I. E., Rey, M., et al. 2020, ApJS, 247, 55, doi: 10.3847/1538-4365/ab7a1a Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2 Helling, C. 2019, Annual Review of Earth and Planetary Sciences, 47, 583, doi: 10.1146/annurev-earth-053018-060401 Herman, M. K., de Mooij, E. J. W., Nugroho, S. K., Gibson, N. P., & Jayawardhana, R. 2022, AJ, 163, 248, doi: 10.3847/1538-3881/ac5f4d Hirano, T., Kuzuhara, M., Kotani, T., et al. 2020, Publications of the Astronomical Society of Japan, 72, doi: 10.1093/pasj/psaa085 Jindal, A., de Mooij, E. J. W., Jayawardhana, R., et al. 2020, AJ, 160, 101, doi: 10.3847/1538-3881/aba1eb Jones, A., Noll, S., Kausch, W., Szyszka, C., & Kimeswenger, S. 2013, A&A, 560, A91, doi: 10.1051/0004-6361/201322433 Kempton, E. M. R., Bean, J. L., Louie, D. R., et al. 2018, PASP, 130, 114401, doi: 10.1088/1538-3873/aadf6f Khalafinejad, S., Molaverdikhani, K., Blecic, J., et al. 2021, A&A, 656, A142, doi: 10.1051/0004-6361/202141191 Kite, E. S., & Schaefer, L. 2021, ApJL, 909, L22, doi: 10.3847/2041-8213/abe7dc Kotani, T., Tamura, M., Nishikawa, J., et al. 2018, in Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, Vol. 10702, International Society for Optics and Photonics (SPIE), 296 – 306, doi: 10.1117/12.2311836 Kreidberg, L. 2015, PASP, 127, 1161, doi: 10.1086/683602 Kreidberg, L., Koll, D. D. B., Morley, C., et al. 2019, Nature, 573, 87, doi: 10.1038/s41586-019-1497-4 Kuzuhara, M., Hirano, T., Kotani, T., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, 1070260, doi: 10.1117/12.2311832 Lee, J.-J., & Gullikson, K. 2016, plp: v2.1 alpha 3, v2.1-alpha.3, Zenodo, doi: 10.5281/zenodo.56067 Libby-Roberts, J. E., Berta-Thompson, Z. K., Diamond-Lowe, H., et al. 2022, AJ, 164, 59, doi: 10.3847/1538-3881/ac75de Mace, G., Sokal, K., Lee, J.-J., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10702, Ground-based and Airborne Instrumentation for Astronomy VII, ed. C. J. Evans, L. Simard, & H. Takami, 107020Q, doi: 10.1117/12.2312345 Madhusudhan, N., Ag´undez, M., Moses, J. I., & Hu, Y. 2016, SSRv, 205, 285, doi: 10.1007/s11214-016-0254-3 Miguel, Y. 2019, MNRAS, 482, 2893, doi: 10.1093/mnras/sty2803 Molli`ere, P., & Snellen, I. A. G. 2019, A&A, 622, A139, doi: 10.1051/0004-6361/201834169 Molli`ere, P., Wardenier, J. P., van Boekel, R., et al. 2019, A&A, 627, A67, doi: 10.1051/0004-6361/201935470 Morris, B. M., Delrez, L., Brandeker, A., et al. 2021, A&A, 653, A173, doi: 10.1051/0004-6361/202140892 Mugnai, L. V., Modirrousta-Galian, D., Edwards, B., et al. 2021, AJ, 161, 284, doi: 10.3847/1538-3881/abf3c3 Mugnai, L. V., Modirrousta-Galian, D., Edwards, B., et al. 2021, AJ, 161, 284, doi: 10.3847/1538-3881/abf3c3 Neves Ribeiro do Amaral, L., Barnes, R., Segura, A., & Luger, R. 2022, arXiv e-prints, arXiv:2203.10127. https://arxiv.org/abs/2203.10127 Noll, S., Kausch, W., Barden, M., et al. 2012, A&A, 543, A92, doi: 10.1051/0004-6361/201219040 Nugroho, S. K., Kawahara, H., Gibson, N. P., et al. 2021, ApJL, 910, L9, doi: 10.3847/2041-8213/abec71 Oliva, E., Origlia, L., Scuderi, S., et al. 2015, A&A, 581, A47, doi: 10.1051/0004-6361/201526291 Ortenzi, G., Noack, L., Sohl, F., et al. 2020, Scientific Reports, 10, 10907, doi: 10.1038/s41598-020-67751-7 Owen, J. E., Shaikhislamov, I. F., Lammer, H., Fossati, L., & Khodachenko, M. L. 2020, SSRv, 216, 129, doi: 10.1007/s11214-020-00756-w Park, C., Jaffe, D. T., Yuk, I.-S., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 91471D, doi: 10.1117/12.2056431 Peacock, S., Barman, T., Shkolnik, E. L., et al. 2020, ApJ, 895, 5, doi: 10.3847/1538-4357/ab893a Quirrenbach, A., Amado, P. J., Caballero, J. A., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay, I. S. McLean, & H. Takami, 91471F, doi: 10.1117/12.2056453 Ramirez, R. M., & Kaltenegger, L. 2014, ApJL, 797, L25, doi: 10.1088/2041-8205/797/2/L25 Ridden-Harper, A. R., Snellen, I. A. G., Keller, C. U., et al. 2016, A&A, 593, A129, doi: 10.1051/0004-6361/201628448 Rogers, J. G., & Owen, J. E. 2021, MNRAS, 503, 1526, doi: 10.1093/mnras/stab529 Rogers, L. A., Bodenheimer, P., Lissauer, J. J., & Seager, S. 2011, ApJ, 738, 59, doi: 10.1088/0004-637X/738/1/59 Rousselot, P., Lidman, C., Cuby, J. G., Moreels, G., & Monnet, G. 2000, A&A, 354, 1134 Schaefer, L., & Fegley, B. 2009, ApJL, 703, L113, doi: 10.1088/0004-637X/703/2/L113 Shkolnik, E. L., & Barman, T. S. 2014, AJ, 148, 64, doi: 10.1088/0004-6256/148/4/64 Sim, C. K., Le, H. A. N., Pak, S., et al. 2014, Advances in Space Research, 53, 1647, doi: 10.1016/j.asr.2014.02.024 Sing, D. K., Fortney, J. J., Nikolov, N., et al. 2016, Nature, 529, 59, doi: 10.1038/nature16068 Snellen, I. A. G., Brandl, B. R., de Kok, R. J., et al. 2014, Nature, 509, 63, doi: 10.1038/nature13253 Snellen, I. A. G., de Kok, R. J., de Mooij, E. J. W., & Albrecht, S. 2010, Nature, 465, 1049, doi: 10.1038/nature09111 Snellen, I. A. G., de Kok, R. J., le Poole, R., Brogi, M., & Birkby, J. 2013, ApJ, 764, 182, doi: 10.1088/0004-637X/764/2/182 Swain, M. R., Estrela, R., Roudier, G. M., et al. 2021, AJ, 161, 213, doi: 10.3847/1538-3881/abe879 Tamura, M., Suto, H., Nishikawa, J., et al. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8446, Ground-based and Airborne Instrumentation for Astronomy IV, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 84461T, doi: 10.1117/12.925885 Tamuz, O., Mazeh, T., & Zucker, S. 2005, MNRAS, 356, 1466, doi: 10.1111/j.1365-2966.2004.08585.x Thompson, M. A., Telus, M., Schaefer, L., et al. 2021, Nature Astronomy, 5, 575, doi: 10.1038/s41550-021-01338-8 Trifonov, T., Caballero, J. A., Morales, J. C., et al. 2021, Science, 371, 1038, doi: 10.1126/science.abd7645 Tsiaras, A., Rocchetto, M., Waldmann, I. P., et al. 2016, ApJ, 820, 99, doi: 10.3847/0004-637X/820/2/99 Vanderspek, R., Huang, C. X., Vanderburg, A., et al. 2019, ApJL, 871, L24, doi: 10.3847/2041-8213/aafb7a Vidal-Madjar, A., Lecavelier des Etangs, A., D´esert, J. M., et al. 2003, Nature, 422, 143, doi: 10.1038/nature01448 Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2 Wakeford, H. R., Lewis, N. K., Fowler, J., et al. 2019, AJ, 157, 11, doi: 10.3847/1538-3881/aaf04d Woitke, P., Helling, C., Hunter, G. H., et al. 2018, A&A, 614, A1, doi: 10.1051/0004-6361/201732193 Wunderlich, F., Godolt, M., Grenfell, J. L., et al. 2019, A&A, 624, A49, doi: 10.1051/0004-6361/201834504 Yurchenko, S. N., & Tennyson, J. 2014, MNRAS, 440, 1649, doi: 10.1093/mnras/stu326 Zhang, M., Knutson, H. A., Wang, L., et al. 2021, AJ, 161, 181, doi: 10.3847/1538-3881/abe382 Zhang, Z., Zhou, Y., Rackham, B. V., & Apai, D. 2018, AJ, 156, 178, doi: 10.3847/1538-3881/aade4f

High-Resolution Transmission Spectroscopy of the Terrestrial Exoplanet GJ 486b: Appendix & Reference

This article was originally published on by Meribah Knight, WPLN/Nashville Public Radio. ProPublica This article was produced in partnership with , a former member of ProPublica’s Local Reporting Network. to get stories like this one as soon as they are published. WPLN Nashville Public Radio Sign up for Dispatches It has been a little over three years since I began my reporting on juvenile justice in Tennessee. Until then, I hadn’t paid much attention to juvenile courts. For a reporter, they’re difficult to cover with any kind of intimacy. They are shrouded in secrecy in a way adult courts are not. The records are sealed. The proceedings are mostly private. And it’s for good reason: The dumb stuff you do as a kid shouldn’t follow you into adulthood. But this privacy has its downside, because it can shield the adults in charge from accountability. And as I soon found out, juvenile justice in the state does need someone — maybe a reporter — to pay attention. Tennessee has 98 juvenile courts and even more juvenile judges. Those judges have a lot of discretion, making decisions on everything from whether to take a case to whether a kid should get locked up and for how long. What’s more, in Tennessee, kids have no right to a jury trial. So, there’s really no check to a judge’s authority in a case — they decide how to interpret the facts and the law. “It’s like the Wild West out here,” one juvenile defense lawyer told me. “Each judge is its own county, some are hard on crime, some are progressive.” I can tell you from my reporting, he’s not wrong. Along with my colleague Ken Armstrong, I embarked on this story, now a podcast, to try to see inside one county’s juvenile court system, where an all-powerful judge and the jailer she appointed were playing by their own rules and the children were caught in the middle. But I learned about other juvenile justice systems along the way. In a county about 20 miles northeast of Nashville, I observed a hearing on a case involving a group of kids, one armed with a BB gun, who had stolen a phone and car keys from a teen couple. The hearing was for a 16-year-old girl who had held onto the stolen phone during the robbery. The assistant district attorney, known for his tough-on-crime approach, had charged her with aggravated robbery and filed a motion to have her case transferred to adult court. Despite pleadings by her attorney — this was the girl’s first offense, she’d been questioned by police without a parent or guardian present and was never read her Miranda rights — the judge granted the transfer. That means that this case, and any subsequent infraction, no matter how minor, would go straight to adult court, where the girl would face the same penalty as a grown-up. When the hearing ended, the girl, who stood less than 5 feet tall, was handcuffed and taken away with a bond set at $10,000. “I about died,” her lawyer told me afterwards. The woman who conceived of juvenile court at the turn of the 20th century, a Chicago social worker named Jane Addams, believed that children could be corrected and rehabilitated because of their young age. And she believed that juvenile courts could step in to help make rehabilitation happen, acting in loco parentis — in place of the parent. Which meant making decisions in the best interest of the child. That was the hope for Sharieka Frazier, the mother of a boy named Quinterrius Frazier, who was featured in our original investigation. They lived in Rutherford County, Tennessee, and when Quinterrius was in his early teens, he started running away, disappearing for a day or longer and hanging out with older kids. It worried Sharieka no end, so she started to track his phone. She’d go knock on doors looking for him. But as Quinterrius got older, his mother began to lose her grip on him. He stopped playing basketball, a sport that had kept him focused and engaged. And he started hanging out with older kids who spent their free time on less wholesome activities. So Sharieka turned to the juvenile justice system for help. She called the police and took out a runaway petition (treated as a warrant) on Quinterrius to get him back home. “I just figured anything’s worth trying at this point,” Sharieka told me. “I was desperate. I didn’t have any help. I didn’t know what else to do. So that was where I turned.” Sharieka isn’t an outlier. I came across a number of cases where it was parents or relatives who made that first call to police, hoping law enforcement would step in to help. As years passed, Quinterrius got sucked deeper and deeper into the juvenile justice system. Rehabilitation didn’t materialize. But a life of incarceration did. When you spend time in juvenile court, you can still see the remnants of this rehabilitative mission. It’s there in the lingo of the court: There are “petitions” or “summons” instead of “warrants,” “juvenile delinquents” instead of “criminal defendants.” Courts make “determinations” on cases rather than handing down “convictions.” There are juvenile “detention centers” instead of juvenile “jails,” youth “development centers” instead of youth “prisons.” But after years of covering the juvenile court system, I’ve come to realize that this belief in treating kids differently isn’t much more than semantics. During my reporting, I saw what amounted to a carbon copy of the adult system. In Rutherford County, where our new podcast takes place, children were wrongfully arrested and jailed illegally for years. At least hundreds, likely thousands of kids, were stripped of their civil rights, arrested and held in jail when their alleged offenses didn’t meet the state’s legal criteria for incarcerating children. Some were then placed in solitary confinement. (You can .) But just how much this juvenile justice system has strayed from Addams’ ideals, and the ways that the officials and court staff are complicit in that, is what kept me reporting on this story for years after the were published. read our 2021 investigation ProPublica articles I needed to clearly understand exactly how this had happened and to let you, the listener, hear directly from the people responsible, the people impacted and the people who tried to fight back. Because this wasn’t some state secret. Some of what was happening to kids in Rutherford County was known to officials. Years before its juvenile court was mired in lawsuits, the federal government had cited it for keeping kids locked up for too long. The county’s juvenile judge, Donna Scott Davenport, typically sentenced kids to two to 10 days in jail for cursing in the courtroom, which was common. Davenport was reprimanded for it, which led to a loss of grant money and some bad PR, but she didn’t seem bothered. “Was I in violation?” she told the local paper. “Heck yes. But am I going to allow a child to cuss anyone out? Heck no.” A 2020 report by Human Rights for Kids, a nonprofit that investigates and reports on the human rights of children, in the country for its inability to protect the rights of children in the justice system. ranked Tennessee one of the worst states When our reporting on Davenport’s behavior came out, the local university where she was an adjunct lecturer . Later she announced she would not seek reelection and . cut ties with her retired at the end of her term “Despite what you may hear about in the media, there is no such thing as ‘scaring a child straight,’” said Kathy Sinback, the longtime administrator in Davidson County’s juvenile court. “The evidence shows that children struggling with behavioral issues have the best outcomes when they are provided with support, encouragement, and positive opportunities — not isolation, fear and shame.” I heard this same sentiment from the young people I spoke to. I met with one young man who was first arrested for truancy at 12. It was right after his mother died of a drug overdose and he was sent to live with a grandmother he barely knew. He wound up spending a decade in prison. “I wasn’t really a bad kid,” he told me, “ I was just a hurt kid.” When I asked a few longtime juvenile court lawyers and administrators what they’d tell families who found themselves in the crosshairs of this system, their advice was to pay attention, ask for representation and don’t expect harsh punishments to fix a kid’s bad behavior. Chris Kleiser, a public defender of kids in Knox County, recommended asking for a lawyer for your child “at the earliest possible stage. And that includes if law enforcement wants to speak to your child before charges are ever brought.” But even access to a lawyer isn’t guaranteed. Recently, the dearth of lawyers available to judges has hit a crisis point in Tennessee. According to the state’s administrative office of the courts, nearly half of all cases with a court-appointed lawyer are in juvenile court, and finding lawyers willing to take these cases is harder than ever. The juvenile judge in Henry County said her list of available lawyers is the shortest it has been in her 17 years on the bench. What’s more, Tennessee’s reimbursement rate for these lawyers — $50 an hour — is the lowest in the country and it hasn’t changed since 1994. Addams conceived of juvenile court with a clear-eyed mission to rehabilitate the child. In Tennessee : Decisions must be “in the best interest of the child.” But, as one lawyer put it to me, “that means whatever the judge thinks.” the juvenile delinquency statute is explicit Some places have explored newer and more child-focused approaches. In Nashville, for example, juvenile Judge Sheila Calloway launched Tennessee’s first restorative justice program for youth offenders, through which children accused of crimes as serious as aggravated burglary, felony theft or even homicide can be diverted away from the court system completely. Instead of meting out justice in a courtroom, a community-based organization guides conversations between victims and offenders, working toward truth and reconciliation and making meaningful amends. “The less we use draconian measures, the more successful we are,” Calloway said when the program launched in 2018. Davenport, who was featured in the podcast, did not subscribe to that point of view. Over and over she said, “​​We don’t punish our children at all. It’s all about treatment.” “I want the children that come in front of me to leave better than they came in,” she said. But Davenport’s actions belied her words. And Tennessee is moving further in Davenport’s direction: Today, state lawmakers are working to make harsher sentences for children more accessible to judges. Waiting in the wings for the next legislative session are a flurry of and increase juvenile punishments. bills that would make it easier to transfer a child to adult court The bills have been criticized by , the and . And the concerns raised about the bills also showed up in my conversations with lawyers, judges and experts, not to mention dozens of people jailed as kids: , educational disruptions, mental health issues, trauma and a . juvenile attorneys ACLU national experts increases in recidivism boatload of other negative outcomes “You want to believe that you can trust your justice system and your judges and with your children,” said Karerra Brewington, who was arrested as a child and whose brother was jailed repeatedly for much of his youth. “But you know, it ruined my life, it ruined my brother’s life.” Another young man, Dylan Geerts, was 15 when he was illegally jailed by Rutherford County for breaking into unlocked cars and stealing a small radio, some loose change, a hat, a phone case and cologne. Dylan had never been arrested before, let alone jailed. But when police took him to the Rutherford County Juvenile Detention Center, staff there locked him up for four days. He proceeded to unravel emotionally and mentally. I spoke with Dylan eight years after his incarceration. He’d become the named plaintiff in a class-action lawsuit against Rutherford County over its illegal policy that jailed so many kids, including himself. (Eventually that suit was settled and Dylan got $25,000 from it.) I asked him what he thought of Davenport’s statement that children should leave the system better than when they arrived. He looked right at me, shook his head and said, “They’re not coming out better than they went in.” Photo by on AbsolutVision Unsplash

I Spent 3 Years Investigating What Happens to the Kids of Rutherford: Here's What I Uncovered

আমি রাদারফোর্ডের বাচ্চাদের কী ঘটে তা তদন্ত করতে 3 বছর কাটিয়েছি: এখানে আমি যা আবিষ্কার করেছি

Ich habe drei Jahre lang untersucht, was mit den Kindern von Rutherford passiert: Folgendes habe ich herausgefunden

Pasé 3 años investigando qué les sucede a los niños de Rutherford: esto es lo que descubrí

J'ai passé 3 ans à enquêter sur ce qui arrive aux enfants de Rutherford : voici ce que j'ai découvert

रदरफोर्ड के बच्चों के साथ क्या होता है इसकी जांच करने में मैंने 3 साल बिताए: यहां बताया गया है कि मैंने क्या उजागर किया

ラザフォードの子供たちに何が起こったのかを 3 年かけて調査しました: これが私が明らかにしたことです

나는 러더퍼드의 아이들에게 무슨 일이 일어나는지 조사하는 데 3년을 보냈습니다. 제가 알아낸 내용은 다음과 같습니다.

Passei 3 anos investigando o que acontece com as crianças de Rutherford: aqui está o que descobri

Я провел три года, расследуя, что происходит с детьми Резерфорда: вот что я обнаружил

Rutherford'un Çocuklarına Ne Olduğunu Araştırmak İçin 3 Yılımı Harcadım: İşte Ortaya Çıkardıklarım

Tôi đã dành 3 năm để điều tra những gì xảy ra với những đứa trẻ ở Rutherford: Đây là những gì tôi đã khám phá ra

我花了三年时间调查卢瑟福孩子们的遭遇：这就是我发现的

Scientific American Supplement, No. 365, December 30, 1882 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . SPECTRUM GRATINGS. here SPECTRUM GRATINGS. At a recent meeting of the London Physical Society, Prof. Rowland, of Baltimore, exhibited a number of his new concave gratings for giving a diffraction spectrum. He explained the theory of their action. Gratings can be ruled on any surface, if the lines are at a proper distance apart and of the proper form. The best surface, however, is a cylindrical or spherical one. The gratings are solid slabs of polished speculum metal ruled with lines equidistant by a special machine of Prof. Rowland's invention. An account of this machine will be published shortly. The number of lines per inch varied in the specimens shown from 5,000 to 42,000, but higher numbers can be engraved by the cutting diamond. The author has designed an ingenious mechanical arrangement for keeping the photographic plates in focus. In this way photographs of great distinctness can be obtained. Prof. Rowland exhibited some 10 inches long, which showed the E line doubled, and the large B group very clearly. Lines are divided by this method which have never been divided before, and the work of photographing takes a mere fraction of the time formerly required. A photographic plate sensitive throughout its length is got by means of a mixture of eosene, iodized collodion, and bromized collodion. Prof. Rowland and Captain Abney, R.E., are at present engaged in preparing a new map of the whole spectrum with a focus of 18 feet. In reply to Mr. Hilger, F.R.A.S., the author stated that if the metal is the true speculum metal used by Lord Rosse, it would stand the effects of climate, he thought; but if too much copper were put in, it might not. In reply to Mr. Warren de la Rue, Prof. Rowland said that 42,000 was the largest number of lines he had yet required to engrave on the metal. Prof. Guthrie read a letter from Captain Abney, pointing out that Prof. Rowland's plates gave clearer spectra than any others; they were free from "ghosts," caused by periodicity in the ruling, and the speculum metal had no particular absorption. Prof. Dewar, F.R.S., observed that Prof. Liveing and he had been engaged for three years past in preparing a map of the ultra-violet spectrum, which would soon be published. He considered the concave gratings to make a new departure in the subject, and that they would have greatly facilitated the preparation of his map. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2006). Scientific American Supplement, No. 365, December 30, 1882. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/18763/pg18763-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

SPECTRUM GRATINGS

Scientific American Supplement, No. 392, July 7, 1883 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . PHOTOGRAPHIC ACTION STUDIED SPECTROSCOPICALLY. here PHOTOGRAPHIC ACTION STUDIED SPECTROSCOPICALLY. At the last meeting of the Chemical Society Captain Abney gave a lecture on the above subject to a large audience. We may premise by saying that the demonstrations he gave were carried out principally by means of experiments on paper, to enable his hearers to understand the different points he wished to enforce. The lecture was commenced by insisting on the fact that all photographic action took place within the molecules of the compound acted upon and not on the molecule itself, and from this he deduced that the absorption of radiation which take place by such compounds is principally caused by the atoms composing the molecule. This was found to be the case in the organic liquids, which the lecturer to some extent had investigated, where he had further traced the absorption to the vibrating atoms of hydrogen in those bodies. In order to properly investigate the action of light it was necessary to ascertain which components of light in the spectrum were the chief agents in causing it, and this led him to consider the means to be employed to obtain a spectrum. The effects of diffraction gratings were first discussed, and in two which were shown it was found that in some spectra the visible portions were dimmed; in others the ultra-violet and the infra-red were almost entirely absent. It thus became necessary to investigate the condition of a grating before placing any confidence in the results obtained. This was the first pitfall into which an experimentalist was liable to fall. If prisms were used for obtaining the spectrum, then precautions had also to be taken, since all glass absorbed a portion of the ultra-violet rays and some the infra-red. On the whole, he considered that the best glass to use was pure white flint glass for the collimator, the prisms, and the camera lens. Another inquiry that was necessary was the source of radiation which it was proposed to use. Diagrams showed the unsatisfactory nature of solar radiation, and a photograph of the whole spectrum, taken with it under certain atmospheric conditions in which the effect of the green rays were almost , demonstrated the false conclusions that might be deduced as to the sensitiveness of any particular compound. nil Captain Abney also showed the satisfactory conditions which existed in using the crater of the positive pole of the electric arc light as a source, and by diagrams illustrated the inferiority of an incandescent light for the purpose, owing to the deficiency of violet and ultra-violet rays. Having thus settled the source of illumination and the kind of apparatus to employ, he next considered the conditions under which the sensitive salts were to be exposed. The action of ordinary sensitizers was explained and demonstrated by experiments, from which point the results of certain colored sensitizers were considered. Thus, various aniline dyes were proved to be bromine absorbents, and likewise, more or less, to be capable of being acted upon by light in those regions of the spectrum they absorbed. The result of the two effects was to produce a developable image of the spectrum just in those parts to which the salt of silver was sensitive, and also in the parts where the dye itself was acted upon. The latter effect was traced to the organic matter being oxidized in the presence of the sensitive silver salt. The sensitizing effect of one silver compound upon another was then gone into, and experiments and photographs showed where two salts of silver were in contact with one another, and without an energetic sensitizer being at hand, that the one when acted upon by light absorbed the halogen liberated from the other through the same cause and that a new molecule was formed. This was of importance, since in photographic spectroscopic researches a conclusion might be arrived at that a body suffered absorption in those regions of the spectrum where this interesting reaction took place, whereas in reality the phenomenon might be due to the silver salts employed. This was another pitfall for the unwary. Again, it became necessary in studying photographic action to make sure that the effect of radiation was only a reducing action, and that the results were not vitiated by some other action. The destruction by oxidizing agents of the effect produced by light was then experimentally demonstrated, and photographs of the spectrum showed that this effect was increased by the action of light itself. Thus, when immersing a plate sensitive to all radiations, visible and invisible, in a very dilute solution of nitric acid, bichromate of potash, or hydroxyl, it was shown that if the plate were exposed to light, first the parts acted upon by the red rays were reduced before the parts not acted upon at all by the spectrum, thus conclusively proving that light itself helped forward the oxidation or so-called solarization of the image. It thus became a struggle, under ordinary circumstances, between the reducing action on the normal salt and the oxidizing action on the altered salt as to which should gain the mastery. If the reducing action of any particular ray were the most active, then a negative image resulted, whereas if the oxidizing action were in the ascendant, a positive image resulted. Thus, in determining the action of light on a particular salt, this antagonism had to be taken into account, and exposure made with such precautions that no oxidizing action could occur, as would be the case if an inorganic sensitizer, such as sulphite of soda, were used. The reversal of the image by soluble haloid salts, such as bromide of potassium, was then dwelt upon with experimental demonstration. It was shown that the merest trace of soluble haloid would reverse an image by the extraction of bromine from it, and the fact that the most refrangible part of the spectrum was principally efficacious in completing this action showed how necessary it was to avoid falling into error when analyzing photographic action by the spectroscope. A reference was next made to gelatine plates, in which, owing to their preparation, reversal through the above cause was most likely to take place, and a plate soaked in sulphite of soda and exposed in the camera for a couple of minutes--a time largely in excess of that necessary to give a reversal under ordinary circumstances--proved the efficacy of the oxygen absorber, the image remaining in its normal condition after development. The lecturer closed his remarks by showing the different molecular states of iodide, bromide, and chloride of silver, as produced by different modes of preparation. The color of the film by transmitted light in every case indicated the effect which was likely to be produced on them, and the photographed spectrum in each of them showed the remarkable differences that were found. The points raised by Captain Abney at different times are well worthy the study of scientific photographers, since strict attention to the modes of exposure to the spectrum, to the instruments employed, and to the source of light used can alone insure accuracy in comparative experiments.-- . Br. Jour. of Photo About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Various (2005). Scientific American Supplement, No. 392, July 7, 1883. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/8742/pg8742-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Stories, News & Mentions about SPEXT

Spext’s Stories on HackerNoon