watchTowr, a cybersecurity startup redefining External Attack Surface Management, announced a $19 million Series A funding round led by Peak XV (formerly known as Sequoia India & Southeast Asia), with repeat participation from Prosus Ventures and Cercano Management.

watchTowr: Cybersecurity Company Secures $19 Million (Series A)

As of the end of the June quarter, Peak XV Partners held a 6.25 percent stake in Five Star Business Finance.

Five Star Business Finance block deal: Equity worth Rs 832 cr sold as Peak XV, other PE investors likely offload 4.65% stake

Austin King, a 22-year-old concession worker at Yellowstone, was last heard from on Sept. 17, when `officials say he called friends and family from the summit of Eagle Peak. Standing at more than 11,300 feet, Eagle Peak is the highest point in the national park and located in its extremely remote ...

Search launched for Yellowstone worker missing in remote part of park

Astronomy fans figuring out where and what time to see the April 8, 2024, total solar eclipse could check out maps showing the path of totality.

Solar eclipse maps show 2024 totality path, peak times and how much of the eclipse people could see across the U.S. - CBS News

The 2024 solar eclipse fascinated millions across the U.S. — and those in the &quot;path of totality&quot; got to experience a few minutes of darkness during its daytime <strong>peak</strong>.

What time the 2024 solar eclipse started, reached peak totality and ended - CBS News

Live coverage of the solar eclipse in Central Florida, including <strong>peak</strong> times, timeline, where to watch, as well as path of totality.

Solar Eclipse 2024 live: Peak times in Orlando, how to watch in Central Florida

Peak

peak

CAYMAN ISLANDS, George Town, December 12th, 2024/Chainwire/--KAST a financial technology platform built on stablecoins to deliver a neobank-style experience, has raised US$10 million in seed funding. The round is led by HSG (HongShan Capital Group) and Peak XV Partners. A number of high-profile angels, partners of DST Global, and Goodwater Capital also joined the round. “For most countries and over half of global GDP, banking does not match the openness and speed of the Internet, it’s fundamentally broken. Stablecoins are the clear solution, but the user experience wasn’t great. We are building KAST to change this”, adds Raagulan Pathy, KAST’s Co-Founder. Pathy is a stablecoin expert, formerly the VP for Asia Pacific & CEO for Singapore at Circle, best known for USDC. KAST offers payment cards and USD-denominated accounts to people in over 150 countries, the ability to receive and send cash, and working with partners such as Bridge (recently acquired by Stripe). KAST will soon launch savings and other remittance products — all without the need to touch fiat directly. The maturing of stablecoin regulation and acceptance has led to rapid integration with traditional finance networks, enabling users to send money faster, cheaper, and more securely. KAST highlighted use cases such as holding USD stablecoins as an alternative to volatile currencies, enabling people to buy imports and travel with a card, and allowing remote workers globally to get paid. “The next generation of neobanks will be built on stablecoins and inherently global by DNA. They will differ fundamentally from the current generation of neobanks, which are limited in countries due to licensing, and struggle in integrating blockchain; their core systems and capabilities are just too different,” comments Daniel Bertoli, co-founder of KAST and former Partner at Quona Capital, a global FinTech fund that focuses on investments in neobanks. KAST has completed a $10 million seed funding round at an undisclosed valuation. Within its first four months, the company has demonstrated strong early traction in terms of user adoption and revenue. The team views this as a reflection of significant global interest in its platform. Stablecoins have experienced remarkable growth with over US$170 billion in supply, and US$8.5 trillion transaction volume in Q2 2024 by over 125 million users. Analysts predict by 2030; user numbers will exceed 1 billion and supply will surpass US$1 trillion. “KAST delivers real benefits to global users. Their USD-denominated accounts provide a safe haven for hard-earned income when local currencies decline, especially for those without access to traditional US accounts. As more digital nomads receive salaries in stablecoins, they can now bypass the hassle of legacy rails. International transfers that once took weeks can now be completed instantly and at virtually no cost,” said Alex Svanevik (CEO/co-founder of Nansen.ai, an early investor in KAST). Users can visit the KAST website to learn more about KAST and discover how their innovative offerings can transform financial experience. Testimonials From Card Users “I spent two weeks at the bank trying to open a simple savings account. They asked for more than 10 documents (~100 papers) and had to sign and stamp each of them. And I opened a crypto card @KAST_official in literally 2min. Why would anyone go after traditional cards anymore? If i don't need my bank statements for embassies, I would always use crypto cards like @KAST_official” - Mardeni (Twitter)“One tap to solve all my issues. That's all it took when I got my KAST card. Here's a quick recap, I've been traveling a lot lately, and exchanging money has always been a hassle. You usually end up losing 5-8% in the process by the time the cash is in your hand. A friend of mine sent me a link and said, "Give this a try!" Fast forward two months, and I've been tapping away quite a lot. Thanks to @KAST_official … I've used it across five countries over the last couple of months without any issues. It's made international spending and crypto usage incredibly simple” - Alpha Bets (Twitter)“I just used my @KAST_official card to pay for my project's domain name and it worked seamlessly all I had to do was fund it with Solana USDC, this is actually a game changer for me and any other thing I'll want to do with it” - Alex (Twitter) About KAST KAST is transforming the financial-technology landscape, by offering a seamless blend of stablecoin technology with traditional payment means. The KAST platform enables USD accounts globally to 150+ countries, reaching over 5 billion people - offering a solution where banking falls short. The silky-smooth user experience is taking mainstream the openness, speed and security of stablecoins; KAST enables users to send, receive, save, and spend funds across borders efficiently. For more information, users can visit www.kast.xyz. Contact VP Global Partnerships Bas KAST Bas@kast.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here CAYMAN ISLANDS, George Town, December 12th, 2024/Chainwire/--KAST a financial technology platform built on stablecoins to deliver a neobank-style experience, has raised US$10 million in seed funding. The round is led by HSG (HongShan Capital Group) and Peak XV Partners. A number of high-profile angels, partners of DST Global, and Goodwater Capital also joined the round. “For most countries and over half of global GDP, banking does not match the openness and speed of the Internet, it’s fundamentally broken. Stablecoins are the clear solution, but the user experience wasn’t great. We are building KAST to change this”, adds Raagulan Pathy, KAST’s Co-Founder. “For most countries and over half of global GDP, banking does not match the openness and speed of the Internet, it’s fundamentally broken. Stablecoins are the clear solution, but the user experience wasn’t great. We are building KAST to change this”, adds Raagulan Pathy, KAST’s Co-Founder. Pathy is a stablecoin expert, formerly the VP for Asia Pacific & CEO for Singapore at Circle, best known for USDC. KAST offers payment cards and USD-denominated accounts to people in over 150 countries, the ability to receive and send cash, and working with partners such as Bridge (recently acquired by Stripe). KAST will soon launch savings and other remittance products — all without the need to touch fiat directly. The maturing of stablecoin regulation and acceptance has led to rapid integration with traditional finance networks, enabling users to send money faster, cheaper, and more securely. KAST highlighted use cases such as holding USD stablecoins as an alternative to volatile currencies, enabling people to buy imports and travel with a card, and allowing remote workers globally to get paid. “The next generation of neobanks will be built on stablecoins and inherently global by DNA. They will differ fundamentally from the current generation of neobanks, which are limited in countries due to licensing, and struggle in integrating blockchain; their core systems and capabilities are just too different,” comments Daniel Bertoli, co-founder of KAST and former Partner at Quona Capital, a global FinTech fund that focuses on investments in neobanks. “The next generation of neobanks will be built on stablecoins and inherently global by DNA. They will differ fundamentally from the current generation of neobanks, which are limited in countries due to licensing, and struggle in integrating blockchain; their core systems and capabilities are just too different,” comments Daniel Bertoli, co-founder of KAST and former Partner at Quona Capital, a global FinTech fund that focuses on investments in neobanks. KAST has completed a $10 million seed funding round at an undisclosed valuation. Within its first four months, the company has demonstrated strong early traction in terms of user adoption and revenue. The team views this as a reflection of significant global interest in its platform. Stablecoins have experienced remarkable growth with over US$170 billion in supply, and US$8.5 trillion transaction volume in Q2 2024 by over 125 million users. Analysts predict by 2030; user numbers will exceed 1 billion and supply will surpass US$1 trillion. growth growth Analysts predict Analysts predict “KAST delivers real benefits to global users. Their USD-denominated accounts provide a safe haven for hard-earned income when local currencies decline, especially for those without access to traditional US accounts. As more digital nomads receive salaries in stablecoins, they can now bypass the hassle of legacy rails. International transfers that once took weeks can now be completed instantly and at virtually no cost,” said Alex Svanevik (CEO/co-founder of Nansen.ai, an early investor in KAST). “KAST delivers real benefits to global users. Their USD-denominated accounts provide a safe haven for hard-earned income when local currencies decline, especially for those without access to traditional US accounts. As more digital nomads receive salaries in stablecoins, they can now bypass the hassle of legacy rails. International transfers that once took weeks can now be completed instantly and at virtually no cost,” said Alex Svanevik (CEO/co-founder of Nansen.ai, an early investor in KAST). Users can visit the KAST website to learn more about KAST and discover how their innovative offerings can transform financial experience. website website Testimonials From Card Users “I spent two weeks at the bank trying to open a simple savings account. They asked for more than 10 documents (~100 papers) and had to sign and stamp each of them. And I opened a crypto card @KAST_official in literally 2min. Why would anyone go after traditional cards anymore? If i don't need my bank statements for embassies, I would always use crypto cards like @KAST_official” - Mardeni ( Twitter )“One tap to solve all my issues. That's all it took when I got my KAST card. Here's a quick recap, I've been traveling a lot lately, and exchanging money has always been a hassle. You usually end up losing 5-8% in the process by the time the cash is in your hand. Twitter Twitter A friend of mine sent me a link and said, "Give this a try!" Fast forward two months, and I've been tapping away quite a lot. Thanks to @KAST_official … I've used it across five countries over the last couple of months without any issues. It's made international spending and crypto usage incredibly simple” - Alpha Bets ( Twitter )“I just used my @KAST_official card to pay for my project's domain name and it worked seamlessly all I had to do was fund it with Solana USDC, this is actually a game changer for me and any other thing I'll want to do with it” - Alex ( Twitter ) Twitter Twitter Twitter Twitter About KAST KAST is transforming the financial-technology landscape, by offering a seamless blend of stablecoin technology with traditional payment means. The KAST platform enables USD accounts globally to 150+ countries, reaching over 5 billion people - offering a solution where banking falls short. KAST KAST The silky-smooth user experience is taking mainstream the openness, speed and security of stablecoins; KAST enables users to send, receive, save, and spend funds across borders efficiently. For more information, users can visit www.kast.xyz . www.kast.xyz www.kast.xyz Contact VP Global Partnerships Bas KAST Bas@kast.xyz 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

KAST Secures US$10 Million Seed Round Led By HSG (HongShan Capital Group) And Peak XV Partners

KAST ukax 10 millones de dólares ukjam jatha muyuntatw HSG (HongShan Capital Group) ukat Peak XV Partners ukanakan p’iqinchata

KAST obtiene una ronda de financiación inicial de 10 millones de dólares liderada por HSG (HongShan Capital Group) y Peak XV Partners

KAST、HSG（HongShan Capital Group）とPeak XV Partnersが主導する1,000万ドルのシードラウンドを確保

KAST нь HSG (HongShan Capital Group) болон Peak XV түншүүдээр удирдуулсан 10 сая долларын үрийг баталгаажуулав.

KAST Mendapat Pusingan Benih AS$10 Juta Diketuai Oleh HSG (HongShan Capital Group) Dan Rakan Kongsi Peak XV

KAST получает начальный раунд финансирования в размере 10 миллионов долларов США под руководством HSG (HongShan Capital Group) и Peak XV Partners

KAST zabezpečuje počiatočné kolo v hodnote 10 miliónov USD vedené HSG (HongShan Capital Group) a partnermi Peak XV

КАСТ обезбеђује 10 милиона долара за почетак рунде предвођени ХСГ (ХонгСхан Цапитал Гроуп) и Пеак КСВ партнерима

HSG (ஹாங்ஷான் கேபிடல் குரூப்) மற்றும் பீக் XV பார்ட்னர்கள் தலைமையிலான KAST US$10 மில்லியன் விதை சுற்று

KAST, HSG (HongShan Capital Group) ve Peak XV Partners Liderliğinde 10 Milyon ABD Doları Değerinde Tohum Turu Elde Etti

KAST забезпечує стартовий раунд у розмірі 10 мільйонів доларів США під керівництвом HSG (HongShan Capital Group) і партнерів Peak XV

KAST đảm bảo vòng hạt giống trị giá 10 triệu đô la Mỹ do HSG (HongShan Capital Group) và Peak XV Partners dẫn đầu

Authors:
(1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia;
(2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.3 Reinforcement Learning 7.4 Search-based Solutions 8 Experimental Setting 9 Related Work 10 Discussion and Conclusion, and References 3 OVERVIEW OF TUNING FRAMEWORK In this study, we break down the entire knob tuning pipeline into five essential components: Workload Characterization, Feature Pruning, Knowledge from Experience, Configuration Recommendation as depicted in Fig. 1. The initial phase, Workload Characterization, is crafted to comprehensively model a workload, utilizing either logical-level queries or embedding runtime metrics for a dynamic understanding of performance indicators Feature Pruning, applied both at the workload-level to minimize execution time and at the configuration-level to streamline the search space, is strategically employed to enhance efficiency through the application of pruning techniques. Knowledge from Experience draws upon historical insights, enabling the tuning algorithm to efficiently converge towards optimal configurations. Finally, Configuration Recommendation involves utilizing diverse techniques, including Bayesian Optimization, Neural networks, Reinforcement learning, and Search-based methods, to generate configurations finely tailored to the specific characteristics of the workload. This paper is available on arxiv under CC BY 4.0 DEED. Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Authors: Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 2 Tuning Objectives 3 Overview of Tuning Framework 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5 Feature Pruning and 5.1 Workload-level Pruning 5.2 Configuration-level Pruning 5.2 Configuration-level Pruning 5.3 Summary 5.3 Summary 6 Knowledge from Experience 6 Knowledge from Experience 7 Configuration Recommendation and 7.1 Bayesian Optimization 7 Configuration Recommendation and 7.1 Bayesian Optimization 7.2 Neural Network 7.2 Neural Network 7.3 Reinforcement Learning 7.3 Reinforcement Learning 7.4 Search-based Solutions 7.4 Search-based Solutions 8 Experimental Setting 8 Experimental Setting 9 Related Work 9 Related Work 10 Discussion and Conclusion, and References 10 Discussion and Conclusion, and References 3 OVERVIEW OF TUNING FRAMEWORK In this study, we break down the entire knob tuning pipeline into five essential components: Workload Characterization, Feature Pruning, Knowledge from Experience, Configuration Recommendation as depicted in Fig. 1. The initial phase, Workload Characterization, is crafted to comprehensively model a workload, utilizing either logical-level queries or embedding runtime metrics for a dynamic understanding of performance indicators Feature Pruning, applied both at the workload-level to minimize execution time and at the configuration-level to streamline the search space, is strategically employed to enhance efficiency through the application of pruning techniques. Knowledge from Experience draws upon historical insights, enabling the tuning algorithm to efficiently converge towards optimal configurations. Finally, Configuration Recommendation involves utilizing diverse techniques, including Bayesian Optimization, Neural networks, Reinforcement learning, and Search-based methods, to generate configurations finely tailored to the specific characteristics of the workload. This paper is available on arxiv under CC BY 4.0 DEED. This paper is available on arxiv under CC BY 4.0 DEED. available on arxiv

How to Tune Your Database Management System for Peak Performance

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 4.1 Recasting Generalised Limits onto B − L Dark Matter To recast our limits on β shown in Fig. 4 into one on the parameter of interest for this model, the gauge coupling strength gB−L, we must define four quantities. Firstly, we must make clear what the quantity A is for this experiment, which will depend on both the model and the signal of interest for this sensor. Secondly, we must choose an observation time, Tobs. Thirdly, we must elaborate on what the concrete noise profile, σ(ω), for this type of experiment is. Lastly, and least importantly following from our discussion above, we must choose a latitude for the experiment. Once these are known, Eq. (2.8) can be re-arranged for gB−L, giving us our model- and experiment-specific 90% CL limit. For B − L coupled dark photon dark matter, the relevant signal is a differential acceleration. This is given by Eq. (2.6) in the time domain, with a now concrete choice for A, Here, gB−L is the gauge coupling strength of the model, ∆ij is the differential B −L charge per nucleon between materials i and j, given by For our observation time, we take Tobs = 10T⊕ to be firmly in the regime where the three peaks can be resolved. For light cavities, which may only be able to remain coherent over the scale of hours rather than days, such a runtime is optimistic. However, since our aim here is merely to showcase how our method can be used concretely and compare to the works of Refs. [36, 38], we do not see this as an issue. Our strategy is general and can be applied to any axial sensor and vector-like DM model, and we have settled on this configuration only for the sake of argument; other sensor technologies, such a magnetically levitated sensors, do not have this issue. Moreover, multiple cavities or data-stacking techniques can be employed to mitigate this issue. Our choices for all of the above parameters except for the laser power, which we expand on below, are summarised in Table 1. These are in keeping with the choices made in Refs. [36, 38]. The choice of where to tune the laser power is critical to give us competitive limits for a wide range of dark matter masses. We have found that we can achieve excellent limits for low dark matter masses, which are of most relevance to our work, by tuning the laser power such that the back-action and shot-noise components are minimised at low We note that this choice of power tuning is different from the strategies usually employed in other studies. Typically, the laser power is tuned so that the measurement-added noise is either minimised on resonance or, as LIGO implements it, well above resonance [88]. However, we found that both of these choices detriments the limit that we can draw at low masses, increasing the background at low frequencies beyond the thermal noise floor. For these other strategies to be beneficial at both the respective frequency targets and at low frequencies, the thermal noise would have to be significantly lower than that achieved with our choice of sensor mass, quality factor, and bath temperature. In the treatment of our backgrounds, we have neglected seismic noise, which can become important at frequencies below ∼ 10 Hz. Since we require the use of two materials to measure the differential acceleration peculiar to B − L DM, we can envision constructing two sensors: one with a moveable mirror made of material i and another of material j. By subtracting their signals, we both isolate the differential acceleration signature and remove backgrounds common to both—this includes the seismic noise component [63] We show our limits in Fig. 5 for three choices of resonance frequency: 1 Hz, 10 Hz and 100 Hz. For all but the last of these frequencies, we are able to exclude new regions of the UDLM B − L parameter space, best excluded by the fifth-force satellite experiment MICROSCOPE [89] and torsion-balance experiment E¨ot-Wash [90]. This showcases the power of such sensors in searching for this DM candidate, as was first pointed out in Ref. [63]. In a single-peak analysis focusing solely on the Compton peak, our limits would be weakened by approximately a factor of 2, as can be seen from Fig. 4. This difference becomes more dramatic for sensors located closer to the equator. We note that σ(ω), though mostly slowly varying in the frequency width ∆ω = ω⊕, does exhibit a large gradient around the resonance frequency of the cavity. In the neighbourhood of this frequency, our assumption that σ(ω) does not vary greatly in the above range is incorrect, and a more careful analysis in which all three peaks take on different noise levels would have to be conducted for more representative limits. However, we do not expect that our limits would differ greatly from our calculation and, at any rate, would still smoothly join to the regimes on either side of the resonance frequency, where our assumption holds. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 4.1 Recasting Generalised Limits onto B − L Dark Matter To recast our limits on β shown in Fig. 4 into one on the parameter of interest for this model, the gauge coupling strength gB−L, we must define four quantities. Firstly, we must make clear what the quantity A is for this experiment, which will depend on both the model and the signal of interest for this sensor. Secondly, we must choose an observation time, Tobs. Thirdly, we must elaborate on what the concrete noise profile, σ(ω), for this type of experiment is. Lastly, and least importantly following from our discussion above, we must choose a latitude for the experiment. Once these are known, Eq. (2.8) can be re-arranged for gB−L, giving us our model- and experiment-specific 90% CL limit. For B − L coupled dark photon dark matter, the relevant signal is a differential acceleration. This is given by Eq. (2.6) in the time domain, with a now concrete choice for A, Here, gB−L is the gauge coupling strength of the model, ∆ij is the differential B −L charge per nucleon between materials i and j, given by For our observation time, we take Tobs = 10T⊕ to be firmly in the regime where the three peaks can be resolved. For light cavities, which may only be able to remain coherent over the scale of hours rather than days, such a runtime is optimistic. However, since our aim here is merely to showcase how our method can be used concretely and compare to the works of Refs. [36, 38], we do not see this as an issue. Our strategy is general and can be applied to any axial sensor and vector-like DM model, and we have settled on this configuration only for the sake of argument; other sensor technologies, such a magnetically levitated sensors, do not have this issue. Moreover, multiple cavities or data-stacking techniques can be employed to mitigate this issue. Our choices for all of the above parameters except for the laser power, which we expand on below, are summarised in Table 1. These are in keeping with the choices made in Refs. [36, 38]. The choice of where to tune the laser power is critical to give us competitive limits for a wide range of dark matter masses. We have found that we can achieve excellent limits for low dark matter masses, which are of most relevance to our work, by tuning the laser power such that the back-action and shot-noise components are minimised at low We note that this choice of power tuning is different from the strategies usually employed in other studies. Typically, the laser power is tuned so that the measurement-added noise is either minimised on resonance or, as LIGO implements it, well above resonance [88]. However, we found that both of these choices detriments the limit that we can draw at low masses, increasing the background at low frequencies beyond the thermal noise floor. For these other strategies to be beneficial at both the respective frequency targets and at low frequencies, the thermal noise would have to be significantly lower than that achieved with our choice of sensor mass, quality factor, and bath temperature. In the treatment of our backgrounds, we have neglected seismic noise, which can become important at frequencies below ∼ 10 Hz. Since we require the use of two materials to measure the differential acceleration peculiar to B − L DM, we can envision constructing two sensors: one with a moveable mirror made of material i and another of material j. By subtracting their signals, we both isolate the differential acceleration signature and remove backgrounds common to both—this includes the seismic noise component [63] We show our limits in Fig. 5 for three choices of resonance frequency: 1 Hz, 10 Hz and 100 Hz. For all but the last of these frequencies, we are able to exclude new regions of the UDLM B − L parameter space, best excluded by the fifth-force satellite experiment MICROSCOPE [89] and torsion-balance experiment E¨ot-Wash [90]. This showcases the power of such sensors in searching for this DM candidate, as was first pointed out in Ref. [63]. In a single-peak analysis focusing solely on the Compton peak, our limits would be weakened by approximately a factor of 2, as can be seen from Fig. 4. This difference becomes more dramatic for sensors located closer to the equator. We note that σ(ω), though mostly slowly varying in the frequency width ∆ω = ω⊕, does exhibit a large gradient around the resonance frequency of the cavity. In the neighbourhood of this frequency, our assumption that σ(ω) does not vary greatly in the above range is incorrect, and a more careful analysis in which all three peaks take on different noise levels would have to be conducted for more representative limits. However, we do not expect that our limits would differ greatly from our calculation and, at any rate, would still smoothly join to the regimes on either side of the resonance frequency, where our assumption holds. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Recasting Three-Peak Exclusion Limits on B − L Dark Matter via Accelerometer Studies

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 4 Application to Accelerometer Studies However, those works that performed a Fourier space analysis did so by only focusing on the Compton peak. As we discussed in Section 3, such an analysis for an axial sensor is sub-optimal, as it fails to capture the full signal and therefore suffers from a signal loss at a range of latitudes. Moreover, ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was illustrated in Fig. 3. Our more holistic threepeak strategy, which also considers this stochasticity, retains the full signal and is largely latitude-independent in its constraining power. Therefore, this choice of sensor and DM model makes for an excellent case study with which to showcase the improved constraining power of our method. Furthermore, a proper vector treatment of the DM field in Fourier space, which includes the stochasticity of the ULDM field variables and the effect of the rotation of the Earth, has not been done. Ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was also pointed out in Refs. [42, 79]. This paper is available on arxiv under CC BY 4.0 DEED license. [9] Here we assume that the sensitivity axis of the detector is locally fixed with respect to the zenith. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 4 Application to Accelerometer Studies However, those works that performed a Fourier space analysis did so by only focusing on the Compton peak. As we discussed in Section 3, such an analysis for an axial sensor is sub-optimal, as it fails to capture the full signal and therefore suffers from a signal loss at a range of latitudes. Moreover, ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was illustrated in Fig. 3. Our more holistic threepeak strategy, which also considers this stochasticity, retains the full signal and is largely latitude-independent in its constraining power. Therefore, this choice of sensor and DM model makes for an excellent case study with which to showcase the improved constraining power of our method. Furthermore, a proper vector treatment of the DM field in Fourier space, which includes the stochasticity of the ULDM field variables and the effect of the rotation of the Earth, has not been done. Ignoring the randomness of the nuisance variables leads to an overly aggressive constraint, as was also pointed out in Refs. [42, 79]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [9] Here we assume that the sensitivity axis of the detector is locally fixed with respect to the zenith.

Enhanced Vector Dark Matter Detection in Accelerometer Studies Through a Three-Peak Analysis

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 3.2 Projected Exclusions To derive our limits, we construct the one-sided log-likelihood-ratio test statistic, defined as where βˆ is that value of β which maximises the likelihood given the observed data set p, characterising the best-fit model. This statistic is defined as a piecewise function as we only expect excess signals to be disfavoured when excluding a value of β in a one-sided test. This corresponds to values of β greater than the best-fit value. Values below this are deemed under-fluctuations and considered consistent with observation. This statistic tells us how consistent the data are with a signal defined by β compared to the best-fit model, with zero representing perfect consistency and large values indicating high inconsistency. Ultimately, we compute 90% CL limits (αconf = 0.90) via a series of Monte Carlo (MC) simulations, following the above procedure in each MC run[8]. For a given choice of β, we We show the 90% CL limit arising from our three-peak analysis in Fig. 4. Also shown are the corresponding results from two single-peak analyses focusing solely on the Compton and either one of the sum or difference peaks. These results follow the same MC procedure as above but take as the full likelihood only the Compton or the sum/difference likelihood given in Eq. (3.2). We see that the three-peak analysis produces a limit that is largely latitude-independent, rising slightly towards the pole. This is because the sensitivity axis at this latitude only has a component parallel to the Earth’s rotation axis and is thus only able to pick out the Compton peak. This latitude-independent limit is in contrast with the analyses that focus on only single peaks, which are both highly sensitive to where the experiment is placed. For the study focusing on the Compton peak, the constraining power is optimal at the pole, where all of the power is contained in the Compton frequency bin, and it rapidly declines towards the equator, where the Compton peak disappears. Note that this and the three-peak results join at this point, with no difference between the approaches. Conversely, for an analysis focusing on one of the sum or difference peaks, the situation is the opposite. In this case, the results of this and the three-peak methods do not converge since half the power is contained in a single one of the sum or difference peaks at the equator; the threepeak analysis captures all of this power, whereas the single-peak analysis misses half of the power. Thus, the strength of our analysis is that the constraining power is retained no matter where an experiment is placed, such that its latitude is rendered largely irrelevant from the viewpoint of constraining a ULDM signal. We emphasize that the interpretation of Fig. 4 is as a set of exclusion lines whereby background pseudodata is generated and the assumed DM signal strength is constrained. The key point is that the level of this constraint depends on the assumption one makes on the nature of the DM signal given a non-detection. Taking this signal to be only a single peak in Fourier space then leads to constraints that are generally dependent on the latitude of the experiment, rapidly weakening towards latitude extremes. On the other hand, employing the full signal model consisting of three peaks yields stronger constraints that are almost independent of the experiment placement. Throughout the above analysis, we assumed that the sensitivity axis pointed in the zenith direction. However, we can relax this assumption and consider what our results would look like if this axis pointed in some different direction, say for instance directions perpendicular to the zenith—namely, the East/West and North/South directions[9]. If North/South-pointing, all of the curves in Fig. 4 would be flipped about the line ϕ = 45◦ . While the strongest limit for the Compton peak would now occur at the equator instead of the pole (and vice versa for both the sum and difference peaks), crucially we would still retain a largely latitude-independent constraint for our three-peak analysis. This is because, throughout the experiment, the directionality of the detector makes a cone, making it sensitive to the vector DM power in all the three directions. If, instead, the axis pointed towards the East/West, we would only see the sum and the difference peaks. This is because, throughout the experimental expedition, the directionality of the detector is restricted to lie on a plane (which would necessarily be perpendicular to the rotation axis of the Earth). Therefore, it is always insensitive to the power contained across the perpendicular direction, which is tied to the standalone Compton peak. One final possibility is when the directionality of the detector traces out a line throughout the experiment. This is only possible when it points parallel to the Earth’s rotation axis (at any given latitude). In this case, we would naturally be oblivious to the Earth’s rotation and hence to the sum and difference peaks, and we would only be able to resolve the Compton peak. In summary, the best-case scenario is when the detector’s sensitivity axis traces out a cone. In this case, we capture all the three peaks since we are sensitive to the vector DM power across all three directions (Earth’s rotation axis and the two orthogonal directions). This paper is available on arxiv under CC BY 4.0 DEED license. [8] While asymptotic formulae exist for computing exclusion limits, we have opted for an MC approach to ensure proper coverage [81]. Since we are following a hybrid frequentist-Bayesian approach, we found this to be an especially pertinent check. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 3.2 Projected Exclusions To derive our limits, we construct the one-sided log-likelihood-ratio test statistic, defined as where βˆ is that value of β which maximises the likelihood given the observed data set p, characterising the best-fit model. This statistic is defined as a piecewise function as we only expect excess signals to be disfavoured when excluding a value of β in a one-sided test. This corresponds to values of β greater than the best-fit value. Values below this are deemed under-fluctuations and considered consistent with observation. This statistic tells us how consistent the data are with a signal defined by β compared to the best-fit model, with zero representing perfect consistency and large values indicating high inconsistency. Ultimately, we compute 90% CL limits (αconf = 0.90) via a series of Monte Carlo (MC) simulations, following the above procedure in each MC run[8]. For a given choice of β, we We show the 90% CL limit arising from our three-peak analysis in Fig. 4. Also shown are the corresponding results from two single-peak analyses focusing solely on the Compton and either one of the sum or difference peaks. These results follow the same MC procedure as above but take as the full likelihood only the Compton or the sum/difference likelihood given in Eq. (3.2). We see that the three-peak analysis produces a limit that is largely latitude-independent, rising slightly towards the pole. This is because the sensitivity axis at this latitude only has a component parallel to the Earth’s rotation axis and is thus only able to pick out the Compton peak. This latitude-independent limit is in contrast with the analyses that focus on only single peaks, which are both highly sensitive to where the experiment is placed. For the study focusing on the Compton peak, the constraining power is optimal at the pole, where all of the power is contained in the Compton frequency bin, and it rapidly declines towards the equator, where the Compton peak disappears. Note that this and the three-peak results join at this point, with no difference between the approaches. Conversely, for an analysis focusing on one of the sum or difference peaks, the situation is the opposite. In this case, the results of this and the three-peak methods do not converge since half the power is contained in a single one of the sum or difference peaks at the equator; the threepeak analysis captures all of this power, whereas the single-peak analysis misses half of the power. Thus, the strength of our analysis is that the constraining power is retained no matter where an experiment is placed, such that its latitude is rendered largely irrelevant from the viewpoint of constraining a ULDM signal. We emphasize that the interpretation of Fig. 4 is as a set of exclusion lines whereby background pseudodata is generated and the assumed DM signal strength is constrained. The key point is that the level of this constraint depends on the assumption one makes on the nature of the DM signal given a non-detection. Taking this signal to be only a single peak in Fourier space then leads to constraints that are generally dependent on the latitude of the experiment, rapidly weakening towards latitude extremes. On the other hand, employing the full signal model consisting of three peaks yields stronger constraints that are almost independent of the experiment placement. Throughout the above analysis, we assumed that the sensitivity axis pointed in the zenith direction. However, we can relax this assumption and consider what our results would look like if this axis pointed in some different direction, say for instance directions perpendicular to the zenith—namely, the East/West and North/South directions[9]. If North/South-pointing, all of the curves in Fig. 4 would be flipped about the line ϕ = 45◦ . While the strongest limit for the Compton peak would now occur at the equator instead of the pole (and vice versa for both the sum and difference peaks), crucially we would still retain a largely latitude-independent constraint for our three-peak analysis. This is because, throughout the experiment, the directionality of the detector makes a cone, making it sensitive to the vector DM power in all the three directions. If, instead, the axis pointed towards the East/West, we would only see the sum and the difference peaks. This is because, throughout the experimental expedition, the directionality of the detector is restricted to lie on a plane (which would necessarily be perpendicular to the rotation axis of the Earth). Therefore, it is always insensitive to the power contained across the perpendicular direction, which is tied to the standalone Compton peak. One final possibility is when the directionality of the detector traces out a line throughout the experiment. This is only possible when it points parallel to the Earth’s rotation axis (at any given latitude). In this case, we would naturally be oblivious to the Earth’s rotation and hence to the sum and difference peaks, and we would only be able to resolve the Compton peak. In summary, the best-case scenario is when the detector’s sensitivity axis traces out a cone. In this case, we capture all the three peaks since we are sensitive to the vector DM power across all three directions (Earth’s rotation axis and the two orthogonal directions). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [8] While asymptotic formulae exist for computing exclusion limits, we have opted for an MC approach to ensure proper coverage [81]. Since we are following a hybrid frequentist-Bayesian approach, we found this to be an especially pertinent check.

Projected Exclusions for Vector Dark Matter Detection Using Latitude-Independent Three-Peak Analysis

Authors:
(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;
(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;
(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;
(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix D The Case of the Gradient of a Scalar 3 Statistical Analysis We now consider the projected exclusion limits that a generic experiment would be able to set using our three-peak analysis. To do this, we use a series of likelihood-ratio tests. 3.1 Signal Likelihood For our likelihood, we follow a hybrid frequentist-Bayesian approach, defining a marginalized likelihood in which all nuisance parameters are integrated out. In our case, these are the random Rayleigh parameters, α, and random uniform DM phases, φ. Such a hybrid approach has already been used in the context of ultralight bosonic dark matter [42, 79]. Our work differs from Ref. [79] since they focused on an axion-like signal as opposed to that from vector DM. It goes beyond Ref. [42] since they did not consider the peaks arising from the rotation of the Earth in their analysis. The full likelihood over all frequency space is then given by the product of the likelihoods in each frequency bin, where pi represents the excess power density in the ith frequency bin, p is the full data vector, and the product runs over all Nbins frequency bins. Ultimately, since our signal only manifests in three bins, it suffices for us to consider only those bins that could potentially contain a signal, and we may ignore all other bins. We can express the likelihood in this way because each bin is statistically uncorrelated, as we show in Appendix C. This is in contrast with the analysis performed in Ref. [45], where a similar study was conducted in the case of the gradient of a scalar in the time domain. There, a complicated covariance matrix had to be computed to account for correlations in the signal at different times. In Fourier space, these covariances disappear. The power of performing this analysis in the frequency domain is thus not only that the signal is contained within a small number of bins, but also that these bins are statistically independent, which allows us to treat the statistics in a significantly simpler way. Crucially, once the latitude of the experiment, ϕ, is fixed, the likelihood depends on the product of all experimental variables via the dimensionless parameter β. This means that we can set a more holistic limit that is independent of the specifics of an experiment. Once the form of A (which depends on both the experiment and the DM model), the observation time Tobs, and the noise profile σ are known, the ensuing limit on β can be recast to one on the model parameters of interest. This makes our analysis, both the results and overall logic, as generally useful as possible. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Authors: Authors: (1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work; (2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work; (3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University; (4) Christopher Tunnell, Department of Physics and Astronomy, Rice University. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Calculating the Stochastic Wave Vector Dark Matter Signal 2.1 The Dark Photon Field 2.1 The Dark Photon Field 2.2 The Detector Signal 2.2 The Detector Signal 3 Statistical Analysis and 3.1 Signal Likelihood 3 Statistical Analysis and 3.1 Signal Likelihood 3.2 Projected Exclusions 3.2 Projected Exclusions 4 Application to Accelerometer Studies 4 Application to Accelerometer Studies 4.1 Recasting Generalised Limits onto B − L Dark Matter 4.1 Recasting Generalised Limits onto B − L Dark Matter 5 Future Directions 5 Future Directions 6 Conclusions, Acknowledgments, and References 6 Conclusions, Acknowledgments, and References A Equipartition between Longitudinal and Transverse Modes A Equipartition between Longitudinal and Transverse Modes B Derivation of Marginal Likelihood with Stochastic Field Amplitude B Derivation of Marginal Likelihood with Stochastic Field Amplitude C Covariance Matrix C Covariance Matrix D The Case of the Gradient of a Scalar D The Case of the Gradient of a Scalar 3 Statistical Analysis We now consider the projected exclusion limits that a generic experiment would be able to set using our three-peak analysis. To do this, we use a series of likelihood-ratio tests. 3.1 Signal Likelihood For our likelihood, we follow a hybrid frequentist-Bayesian approach, defining a marginalized likelihood in which all nuisance parameters are integrated out. In our case, these are the random Rayleigh parameters, α, and random uniform DM phases, φ. Such a hybrid approach has already been used in the context of ultralight bosonic dark matter [42, 79]. Our work differs from Ref. [79] since they focused on an axion-like signal as opposed to that from vector DM. It goes beyond Ref. [42] since they did not consider the peaks arising from the rotation of the Earth in their analysis. The full likelihood over all frequency space is then given by the product of the likelihoods in each frequency bin, where pi represents the excess power density in the i th frequency bin, p is the full data vector, and the product runs over all Nbins frequency bins. Ultimately, since our signal only manifests in three bins, it suffices for us to consider only those bins that could potentially contain a signal, and we may ignore all other bins. We can express the likelihood in this way because each bin is statistically uncorrelated, as we show in Appendix C. This is in contrast with the analysis performed in Ref. [45], where a similar study was conducted in the case of the gradient of a scalar in the time domain. There, a complicated covariance matrix had to be computed to account for correlations in the signal at different times. In Fourier space, these covariances disappear. The power of performing this analysis in the frequency domain is thus not only that the signal is contained within a small number of bins, but also that these bins are statistically independent, which allows us to treat the statistics in a significantly simpler way. th Crucially, once the latitude of the experiment, ϕ, is fixed, the likelihood depends on the product of all experimental variables via the dimensionless parameter β. This means that we can set a more holistic limit that is independent of the specifics of an experiment. Once the form of A (which depends on both the experiment and the DM model), the observation time Tobs, and the noise profile σ are known, the ensuing limit on β can be recast to one on the model parameters of interest. This makes our analysis, both the results and overall logic, as generally useful as possible. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Using Three-Peak Analysis for Projected Exclusion Limits in Vector Dark Matter Detection

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MEXC's Initial Listing Sees 2,471% Peak Surge As GOAT Surpasses $800M Market Cap

Inicijalna kotacija MEXC-a bilježi najveći porast od 2,471% jer GOAT premašuje tržišnu kapitalizaciju od 800 miliona dolara

La cotización inicial de MEXC registra un aumento máximo del 2,471 % mientras GOAT supera la capitalización de mercado de $800 millones

MEXC-en hasierako zerrendak % 2,471eko gorakada gorena ikusten du GOATek 800 milioi dolarreko merkatu-kapitala gainditzen duelako

הרישום הראשוני של MEXC ראה זינוק שיא של 2,471% כאשר GOAT עולה על שווי השוק של 800 מיליון דולר

MEXC की आरंभिक लिस्टिंग में 2,471% की उछाल देखी गई, क्योंकि GOAT ने $800M मार्केट कैप को पार कर लिया

MEXC の新規上場は 2,471% の急上昇を記録し、GOAT の時価総額は 8 億ドルを突破

MEXC pirminiame sąraše didžiausias padidėjimas yra 2 471 %, nes GOAT viršija 800 mln.

MEXC को प्रारम्भिक सूचीले 2,471% शिखर वृद्धि देख्यो किनकि GOAT ले $800M मार्केट क्याप पार गर्यो

Lenaneo la Mathomo la MEXC le Bona 2,471% Peak Surge Ge GOAT e Feta $800M Market Cap

Początkowa oferta MEXC odnotowała wzrost o 2471%, ponieważ kapitalizacja rynkowa GOAT przekroczyła 800 mln USD

Рӯйхати ибтидоии MEXC афзоиши қуллаи 2,471% -ро мебинад, зеро GOAT аз арзиши бозор 800 миллион доллар гузашт.

Uhlu Lokuqala Lwe-MEXC Lubona Ukuphakama Okungu-2,471% Njengoba Imbuzi Idlula U-$800M Imakethe Yemakethe

Unlocking Your Hidden Potential: 5 Practices to Reach Your Peak Performance

Hey there, fellow tech enthusiasts! My name is and today, I want to share with you some insides of mine. As a seasoned developer who's seen the ebb and flow of the digital tide, let's dive into the swirling vortex of what the future might hold for us in 2024. Grab your digital crystal ball, folks, because here come some spicy predictions! Alex Cloudstar, The Era of Augmented Reality 🌐 Picture this: virtual worlds blending seamlessly with our reality. That’s right, folks, AR is about to take a massive leap in 2024. From enhancing customer experiences in retail to transforming education, this tech is set to reshape how we interact with the world. Get ready for a mix of Pokémon GO-like experiences and practical applications that’ll blow your socks off! But I can’t say that I am a fan of this. Just like with other forms of technology, there's a risk of over-reliance and addiction. People might become so engrossed in AR experiences that they neglect real-world responsibilities or become detached from reality. While AR can create immersive experiences, it might also lead to further social detachment. If individuals start preferring virtual interactions over real-world interactions, it could potentially worsen issues related to social isolation and decrease physical human contact. AI, AI, AI Everywhere 🤖 Artificial Intelligence is no longer the next big thing; it's the now big thing. In 2024, we'll see AI continue to weave itself into our daily lives, from predictive analytics guiding business decisions to AI-powered assistants handling more complex tasks. But fear not, humans, for we shall remain the masters behind the machines! Quantum Computing: Unlocking the Unthinkable 🔐 Hold onto your hats, folks, because quantum computing is inching closer to reality in 2024. We're talking about mind-boggling computational power that could revolutionize industries, from healthcare to finance, solving problems that were once deemed unsolvable. It's like upgrading from a bicycle to a warp-speed spaceship! Sustainable Tech 🌱 With great tech comes great responsibility. In 2024, the tech world is taking a giant leap toward sustainability. From eco-friendly data centers to renewable energy-powered devices, the focus on reducing our digital carbon footprint is becoming more crucial than ever. Agrotech and Food Sustainability are essential. Technologies focusing on sustainable agriculture practices, precision farming, and resource-efficient techniques like vertical farming or smart irrigation play a crucial role in ensuring food security while reducing environmental impact. The Rise of Privacy Tech 🔒 Privacy concerns have been making headlines for years, and in 2024, they're taking center stage. As users become more aware of their data rights, we'll witness a surge in privacy-focused tech. Encryption, decentralized systems, and user-centric platforms will shine as guardians of our digital identities. Privacy is the new black, my friends! Now, you might be wondering, "How do I prepare for this tech revolution?" Well, fear not, intrepid developers, for I've got a few tips up my sleeve: Embrace change like a long-lost friend. Keep learning, exploring new technologies, and adapting like a chameleon in a digital jungle. Stay Agile, Stay Relevant: Engage with the tech community. Attend conferences, join forums, and collaborate on projects. The collective wisdom of the crowd is a treasure trove of insights. Community is Key: With great tech comes great vulnerabilities. Prioritize security in your code and stay updated on the latest cybersecurity trends. Focus on Security: As tech evolves, consider the ethical implications of your creations. Build responsibly, with an eye toward the greater good. Ethics Matter: The future is unwritten. Embrace uncertainty, for it’s where innovation thrives. Embrace the Unknown: So, dear readers, fasten your seatbelts, charge your devices, and get ready for a rollercoaster ride through the tech-scape of 2024. It's a thrilling time to be a part of this ever-evolving industry! Until next time, happy coding, and may your bugs be minimal and your breakthroughs monumental!

Tech Predictions for 2024: Taking A Peak Into a Senior Developer's Crystal Ball

According to SalesCycle, Black Friday and Cyber Monday in-store traffic show a . Companies should get their infrastructure ready for peak days since even a few minutes of downtime can cost them thousands of dollars. Over the last few years, many big brands, including , lost a lot of money and broke their customers’ trust because their teams weren’t ready for Black Friday. Circling back to the 38% increase, such a change demands unwavering attention to the provided service and its scalability for the companies to keep up with the flow of orders, maximise customers’ experience, and minimise their negative feedback. 38% increase Walmart, J. Crew, Lowe’s and GAME a of local brands and floral businesses, has its own 3-4 peak days (Valentine’s Day, Mother’s Day) when traffic is increasing 30X. Our IT team has developed a flexible system that helps to scale the service, manage it, and wrap it up when the peak day is over. Flowwow, global marketplace In this article, I am sharing five tips that will help prepare your service for massive scaling – and avoid mistakes, common for e-commerce platforms. Planning is the key Before any action, develop and implement a detailed scaling plan, divided into several stages: 1 month before a peak day, 2 weeks before a peak day, 3-4 days before a peak day, and 1 day before a peak day with a precise list of actions for the teams. At every stage, it’s important that designated specialists understand how to carry out each task and solve any potential issue. These teams usually consist of a DevOps team and backend developers. #1 Forecast the data Every year the total number of orders is increasing – it comes as no surprise, and that's why it’s vital to forecast the traffic volume and be ready for it. Historical data and cross-functional collaboration with the marketing team are core elements for an accurate prediction. They help to understand which of the system components can grow and then decide on the number of servers and their capacity. Once you have evaluated the expected numbers, put 2X into the forecast. If you predict 30X growth, then set the task to be ready for a 60X increase. This approach allows you extra preparation for unexpected challenges. If you have cloud scaling, you have the tools to dramatically increase the capacity in a short period of time and then reduce it to regular indicators when needed. Do an infrastructure discovery analysis, based on the basic parameters – map out servers, their current size, their components, projects, and applications. Understanding the basic parameters helps extrapolation: you are predicting growth and calculating potential scenarios in case it's indeed 60X. Apply rule 30X to all errors. If you find an error, ask yourself: “If this error starts appearing 30 times more often, will it be a problem for us?” If an error on the 30X traffic is worth being corrected now, start working on it. #2 Protect your scaling team from business requests When your development team is not big enough, the chances are high that everyone's to-do list is full to the brim. Hence, it is necessary to form a separate internal development team, whose focus will exclusively be on the peak period: this team should be free from other business requests and deal only with optimization tasks. The Pareto principle works well here: optimising 20% of the bottlenecks means closing 80% of performance problems in code and the database. Don’t forget to announce a feature freeze (a period when your team won’t add new features) and remind the team about it, preferably a month in advance. We don’t allow making changes to the code or database during this period because, when changes are made, the entire system may fall, and the cost of this error increases 30X at the time. #3 Find bottlenecks and start the optimization Bottlenecks show the mechanisms you need to optimise in your current infrastructure. We divide the current system components into different areas and monitor them separately. It allows us to evaluate the performance of each server and understand which components need more resources from our side – this approach helps to plan our resources flexibly. Use an advanced monitoring system, capable of assessing each component’s performance in real-time, and log aggregation to catch all errors (so you can fix them later). The Pareto principle works well here: optimising 20% of the bottlenecks means closing 80% of performance problems in code and the database. #4 Perform personalised stress testing Perform stress testing in advance, trying to mimic your potential customer behaviour. Start with simulating the infrastructure load, for example, 10X of the usual. Based on the data obtained, we understand, which areas require optimization. There are special tools that allow you to take an hour load and play it 10 times faster to assess and confirm whether the system can withstand a certain level of pressure. #5 Plan for Day X During peak loads, we meticulously schedule the workload of each team member, assigning key roles and distributing responsibilities. We do care about our employees, and that’s why we make sure everyone gets a healthy amount of sleep, however, we also strive to cover the maximum number of time zones. At each stage, we nominate a process manager, who monitors what is happening at the peak moment. It is this person who informs the team if one of the system components approaches the performance limit (80%). As we work remotely, when Day X comes, it is crucial that the whole team keeps in touch not only through work messengers like Slack, but also through emergency communication channels, such as Telegram and a personal phone. Peak Days like Black Friday, Cyber Monday, and Christmas are active phases that you should be ready for. More traffic means more value for your business. This, in turn, usually manifests into increased revenue. That’s why do not hesitate to invest in scaling, adhere to international principles and implement modern services. Keep an eye on new solutions that meet the needs of your business today, and scale with the benefit to your service!

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