Evernest started at the beginning of the 2008 economic crisis by default

Evernest

A concrete example showcasing the performance of JavaScript Maps. This article is not intended to explore the API of the Map object in details. If you’re looking for such a source, please check out . MDN In general, the Map data structure is useful when we want to retrieve/add/delete values through a set of unique keys. One of the characteristics that make JavaScript Map data structure unique compared to its implementation in other languages, is the fact that it remembers the insertion order of its keys. In this article, we will take a close look at what makes this specific feature very useful and why you should use Maps over other data structures in some cases. To do so, we will look at a concrete example by building a shopping basket. Problem: Building Shopping Basket Initially, we have a list of shop items and an empty shopping basket as follows: // initial shop_items shop_items: [ { : "👕", : 10 }, value stock { : "👖", : 10 }, value stock { : "👞", : 10} value stock ] // initial empty shopping_basket shopping_basket: empty We want to make it possible to add items to our shopping basket and to put them back. Adding an item to the shopping basket should: Decrement the stock for that particular item. If the item was never added to the basket, it should add it to the basket with a count of 1 If the item already exists in the basket, it should increment its count in the basket. Putting back an item to the shop items should: Increment the stock for that particular item. If the item has a count bigger than 1 in the basket, it should decrement its count. If the item has a count of 1 in the basket, it should remove it from the basket. Extra requirement: we need to make sure that items in the shopping basket are kept in the order they were inserted. Solution Using Arrays First, let’s see how we can solve it using the shopping basket as an array. Initially, we have the following: // initial shop_items let shop_items = [ { : "👕", : 10 }, value stock { : "👖", : 10}, value stock { : "👞", : 10} value stock ]; // initial empty shopping_basket let shopping_basket = []; Let’s then create a function that adds a specific item from the shop items to the shopping basket: addToBasket Let’s also add a function to remove put back an item from the shopping basket to the shop items list: removeFromBasket Testing our implementation gives the following: // (1) addToBasket("👕");addToBasket("👕"); // Output: shop_items: [ { : "👕", : }, value stock 8 { : "👖", : 10 }, value stock { : "👞", : 10} value stock ] shopping_basket: [ { : "👕", : ] value count 2 } // (2) addToBasket("👞"); // Output: shop_items: [ { : "👕", : }, value stock 8 { : "👖", : 10 }, value stock { : "👞", : } value stock ] shopping_basket: [ { : "👕", : }, value count 2 { value: "👞", count: 1} ] // ------------------------------------ // // (3) removeFromBasket("👕"); // Output: shop_items: [ { : "👕", : }, value stock 9 { : "👖", : 10 }, value stock { : "👞", : } value stock 9 ] shopping_basket: [ { : "👕", : }, value count 1 { value: "👞", count: 1} ] // ------------------------------------ // // (4) removeFromBasket("👞"); // Output: shop_items: [ { : "👕", : }, value stock 9 { : "👖", : 10 }, value stock { : "👞", : } value stock 10 ] // initial empty shopping_basket shopping_basket: [ { : "👕", : } ] value count 1 So, our implementation works ?! What’s the problem then? 🤔 If we give a closer look at both our update functions, we will notice that we are using when searching for an item in the basket. This results in a linear time complexity of O(N). In other words, in the worst case, we will iterate through the entire shop items list and shopping basket twice in the same function. findIndex In the current context, where both sizes are very small and the use of both functions is pretty simple, this doesn’t really cause any performance issue. However, this does not always scale. The complexity can easily grow to O(n²) if we ever nest our functions in another loop. Here’s why Map can solve our issues: The complexity for adding/retrieving/removing items is constant O(1). It has more readable and suited functions for adding/retrieving/removing items. Initially, we have the following: // initial shop_items let shop_items = [ { : "👕", : 10 }, value stock { : "👖", : 10}, value stock { : "👞", : 10} value stock ]; // initial empty shopping_basket let shopping_basket = ; new Map() Let’s then refactor the function: addToBasket Let’s also refactor the function: removeFromBasket If we run the test again, we should see the same results. Why not just use Objects? One might think that the previous performance can also be achieved if we use objects instead of maps. The implementation of would look as follows: addToBasket The implementation of would look as follows: removeFromBasket Although this implementation works as well, it has the following problems: According to , objects keys are not necessarily ordered. MDN The delete operation does not have a constant complexity O(1)! To wrap it up 🙌 I hope you find this article useful enough to understand the performance benefits of JavaScript Maps. Hopefully, this will motivate you to use them in similar use cases. I will also love to hear your feedback or questions if you have any. P.S: you can find me over on twitter @seif_ghezala :)

JavaScript’s Maps For Better Performance

Need to provide database self-service to internal teams? Can't spare the resources to build and maintain your own private DBaaS solution? Sick of cloud DBaaS providers locking you in? is a cloud-native database platform to deploy and manage enterprise-grade PostgreSQL, MongoDB and MySQL database clusters. Percona Everest With Percona Everest, you can: Enable DBA teams to regain control over data and database configuration Empower developers to deploy code faster and self-service provision highly performant, production-ready database clusters Free your entire organization from vendor lock-in and restrictive subscriptions Benefits of Percona Everest Truly , Percona Everest offers the benefits of automated database provisioning and management without the need to build or maintain an in-house database management platform. open source No data lock-in or restrictive contracts Reduced total cost of ownership (TCO) Complete data sovereignty to meet any compliance requirement Fully customizable database configurations Right-sized database deployments Frictionless developer self-service Backing of highly skilled database management experts Key features of Percona Everest Database management via a single pane of glass Percona Everest enables you to manage complex database environments through a single pane of glass or API. Create, configure, deploy, update, and upgrade with zero downtime Backup, restore, restart, suspend/resume, or delete database clusters Complete customization Customize load balancing settings, settings for network exposure, and resource settings like node count, instance type, CPU, memory, storage, etc. Standardized database deployments Create ready-to-use database clusters of enterprise-grade MySQL, PostgreSQL, and MongoDB, with the ability to customize to support a variety of topologies or deployments. Percona Everest leverages to deploy Cloud-Native Percona Distributions. Percona Operators Built-in observability tools Benefit from built-in open source infrastructure monitoring to keep control of (and optimize) resource usage, health, and performance of database clusters. How Percona Everest is designed Percona Everest has two primary components Percona Everest application with the UI On the frontend, there is a Percona Everest UI which is developed using the Vite framework, React library and TypeScript language. On the backend, requests from the frontend app are processed by Percona Everest Backend. It is an API developed in Golang using the Echo framework that sends requests to the Kubernetes API. You can also use the API directly for your needs. Percona Everest CLI (everestctl) You can install Percona Everest operators and components to your Kubernetes Cluster with the help of a console tool Percona Everest CLI (everestctl). Then, Percona Everest will create and manage database clusters. Everestctl is developed in Golang, and it is a built-in executable file. Percona Everest does not supply a Kubernetes cluster; you’ll need to use your own for the deployment. Percona Everest is currently in Beta We invite you to become an early adopter and contribute to progress! Your feedback is crucial to enhancing the software, and we highly value and rely on your input. Percona Everest website Documentation Quick Start Also published . here

A New Way to Provision Databases on Kubernetes

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 3. ANALYSIS AND VALIDATION 3.1. Stellar Host Characterization 3.1.1. Semi-empirical Stellar Parameters We constructed the spectral energy distribution (SED) of EPIC 249631677 using photometric magnitudes from Gaia (GBP and GRP ; Gaia Collaboration et al. 2018) and the AllWISE source catalog (J, H, Ks, W1, W2, and W3; Cutri et al. 2013). The corresponding fluxes for these magnitudes are tabulated on VizieR (Ochsenbein et al. 2000) and shown in Figure 5 and Table 1. The parallax of EPIC 249631677 is π = 17.61 ± 0.09 mas, which yields a distance of 56.8 ± 0.3 pc (Gaia Collaboration et al. 2018; Stassun & Torres 2018). We then derived the stellar luminosity L∗ by integrating over the SED, which yielded L∗ = 0.0041 ± 0.0001L . Two independent methods were applied to obtain stellar mass. First, we used the empirical M∗−MKs relation (applying the metallicity obtained in Section 3.1.2) from Mann et al. (2019) to obtain M∗ = 0.1721 ± 0.0044M . We also applied stellar evolution modeling, using the models presented in Fernandes et al. (2019), using as a constraint the luminosity inferred above and the metallicity derived in Sect. 3.1.2). We considered the stellar age to be >1 Gyr in the absence of signs of youth, such as presence of prominent flares (Ilin et al. 2019, see Section 3.1.3). We obtained with this method M∗ = 0.176 ± 0.004 M . This uncertainty reflects the error propagation on the stellar luminosity and metallicity, but also the uncertainty associated with the input physics of the stellar models. We combined these two mass estimates as in Van Grootel et al. (2018) to obtain M∗ = 0.174±0.004M as our best estimate for the stellar mass of EPIC 249631677. Given its proximity, we expect minimal extinction (Av) for the target; the SED fitting analysis described in Section 3.2.1 similarly constrains it to be 0.02 at 3σ confidence and we adopt that upper limit here. Finally, we note that given its luminosity, mass, and Gaia colors this star is likely to be fully convective (Jao et al. 2018; Rabus et al. 2019). Due to the absence of a strong constraint on the stellar density from the transits, we further obtained stellar radius, surface gravity, effective temperature and density from our evolutionary models. Table 1 summarizes the results from this analysis, along with other properties of the star. Our values are consistent with those listed in the TESS Input Catalog (Stassun et al. 2019), and we adopt them for the remainder of this analysis. 3.1.2. Reconaissance Spectroscopy To confirm EPIC 249631677’s stellar properties and better characterize the system, we acquired an optical spectrum using Keck/HIRES (Vogt et al. 1994) on UT 30 May 2020. The observation took place in 0.6 00 effective seeing and using the C2 decker without the HIRES iodine gas cell, giving an effective resolution of λ/∆λ ≈ 55, 000 from 3600 ˚A to 7990 ˚A. We exposed for 1800 s and obtained SNR of roughly 23 per pixel. Data reduction followed the standard approach of the California Planet Search consortium (Howard et al. 2010). We used our Keck/HIRES radial velocity and Gaia DR2 data to estimate the 3D galactic (UV W) space velocity using the online kinematics calculator[2] of Rodriguez (2016). Following Chubak et al. (2012), our Keck/HIRES spectrum gives a barycentric radial velocity of 6.25±0.17 km s−1 . With the Gaia-derived coordinates, proper motion, and distance listed in Table 1, we find (U, V, W) values of (−17.02, −9.06, +33.66) km s−1 , indicating a likely membership in the Milky Way’s thin disk (Bensby et al. 2014). Using the SpecMatch-Empirical algorithm (Yee et al. 2017), we derive from our HIRES spectrum stellar parameters of Teff = 3195 ± 70 K, R∗ = 0.23 ± 0.10R , and [Fe/H]= −0.24 ± 0.09, consistent with the values tabulated in Table 1. The three best-matching stars in the SpecMatch-Empirical template library are GJ 15B, GJ 447, and GJ 725B, which have spectral types of M3.5V, M4V, and M3.5V, respectively. Given the close match between the spectra of these stars and our target (see Fig. 4), we therefore classify EPIC 249631677 as an M dwarf with subclass 3.5±0.5. We see no evidence of emission line cores at Hα, consistent with our determination that our target is not a young star. We see no evidence of spectral broadening compared to these three stars (which all have v sin i < 2.5 km s−1 ; Reiners et al. 2012), so we set an upper limit on EPIC 249631677’s projected rotational velocity of < 5 km s−1 , comparable to the spectral resolution of HIRES. 3.1.3. Stellar Variability The long-term variations apparent in the everest light curve (Figure 1) are not evident in light curves from other reduction pipelines (e.g., K2SFF). These variations likely arise from systematics and are not reliable for estimating the stellar rotation period (Esselstein et al. 2018). Similarly, no flares are apparent either in the K2 or SPECULOOS data. Flare rates peak for ∼M3.5 stars in TESS data (G¨unther et al. 2020). However, given the long integration time of 29.4 minutes as well as a need for data processing which corrects for the saw-tooth pattern, flare signals, unless very prominent, are expected to be difficult to detect in K2 long cadence data. 3.2. Vetting In order to produce a transit depth on the level of 0.2% in the light curve of the primary target, a background eclipsing binary producing eclipses with depths of 25% to 50% would have to be 5.25 to 6.0 mag fainter than the target, respectively. Qualitatively, the odds of EPIC 249631677 hosting a planet are higher than the odds of such magnitude contrast eclipsing binary being present within the SPECULOOS aperture, given occurrence rates of M-dwarf planets (Dressing & Charbonneau 2013; Mulders et al. 2015; Hardegree-Ullman et al. 2019). A stringent quantitative constraint can be placed using the ingress/egress duration (T12/T34) compared to the total transit duration (T14) (Seager & Mall´enOrnelas 2003, Equation 21). Such a test yields an upper limit on the relative radius of the transiting body. By assuming equal effective surface temperatures, the lower magnitude limit ∆m (corresponding to a flux difference ∆F) for a blended binary mimicking a signal of depth δ is given by: Using the posterior for the transit fit (See Section 3.3), we find for EPIC 249631677 that such a background object can be fainter at most by 1.73 mag at the 3σ level. Fortunately, EPIC 249631677 has a significant proper motion, ∼140 mas yr−1 , which allows us to investigate the presence of background sources at its current sky position. We looked at archival imaging of EPIC 246331677 going back to 1953[3] . A POSS I plate from 1953 is the publicly available oldest image of EPIC 249631677, and it does not show any background source at the current position of the target as shown in Figure 6. The plate is sensitive to objects at least 3.5 magnitudes fainter than the target. Similarly, the Hubble Guide Star Catalogue (GSC), with a limiting magnitude of 20 (Lasker et al. 1990), does not show any background source. While POSS II would go the deepest in terms of limiting magnitude (20.8, Reid et al. 1991), the star has moved appreciably closer to its current location, precluding a definitive measurement from this image. Overall, using archival images we can rule out the possibility of the transit signal originating from background star at a high level of confidence. 3.2.1. Binarity of the Host Star Despite the lack of background sources, the host star could produce a false-positive transit signal if it were a grazing eclipsing binary or a hierarchical eclipsing binary. We investigated the evidence for host star binarity using the isochrones software package (Morton 2015), which performs isochrone fitting in the context of the MESA (Paxton et al. 2011, 2013, 2015) Isochrones and Stellar Tracks database (Dotter 2016; Choi et al. 2016). Single-star and binary evolutionary models are available within isochrones, and the inference is performed via the nested sampling algorithm MULTINEST (Feroz et al. 2009) (as implemented in the PyMultiNest software package (Buchner et al. 2014)), which allows for direct comparisons of the Bayesian evidence ln Z. We tested both single-star and binary models using the priors on photometric magnitudes and stellar distance described in Section 3.1.1. The inferred properties from the single-star model fit are consistent with those given in Table 1 at the 2σ level. The ln Z for the single-star model is −213.86±0.04, whereas the ln Z for the binary model is −229.6 ± 0.2. According to Kass & Raftery (1995), the corresponding Bayes factor of 16 indicates “decisive” evidence in favor of the single-star model. We also examined our Keck/HIRES spectrum for secondary lines that would indicate the presence of another star following the approach of Kolbl et al. (2015). We found no evidence of additional lines down to the method’s standard sensitivity limit of ∆V = 5 mag for ∆v > 10 km s−1 , consistent with EPIC 249631677 being a single, isolated star. We therefore conclude that the available data strongly support EPIC 246331677 being a single star. 3.2.2. Photometric Tests We performed a series of tests on the photometric data to rule out false-positive scenarios. First, we performed an even-odd test on the target using K2 photometry. The even and odd transits are consistent with one another in transit depth to within 1σ. We also looked for secondary eclipses in the phase-folded light curve and found none to be present. Note that since we observe consistent signals in both the K2 and SPECULOOS data sets, we can rule out the signal originating from systematics. The transit depth in SPECULOOS observations with I+z filter, which is redder than Kepler bandpass, are consistent to K2 transit depths within 1σ level, keeping up with the expectation of the achromatic nature of planetary transit. Furthermore, a massive companion, such as a faint white dwarf, can be ruled out using the ellipsoidal variation, which puts a 3σ upper limit on the mass of any companions at the given orbital period of the transit signal as ∼100 MJup (Morris 1985; Niraula et al. 2018). From the transit fit, we can rule out a grazing eclipse originating from a larger transiting object (i.e. ≥ 2R⊕) at >3σ confidence. Together, these tests rule out the object at 3.14 days being a massive companion. 3.3. Transit Fitting We used the refined estimates of the host properties together with a joint analysis of the K2 and SPECULOOS light curves to derive the planetary properties. In order to calculate the transit model, we used batman (Kreidberg 2015). We simultaneously model both the K2 observation as well as the ground-based observations with 21 parameters in a Monte Carlo Markov Chain (MCMC) framework using the emcee package (ForemanMackey et al. 2013). We use a Gaussian prior on the scaled semi-major axis of the orbit a/R∗ of N (25.72, 0.27), derived using Eq. 30 from Winn (2010) along with the stellar density of 32.6 ± 1.0 g cm−3 (see Section 3.1.1) and orbital period of 3.1443 days from the TLS search. As for the limb darkening, we use the non-informative q1, q2 parameterization of the quadratic limb-darkening law as suggested by Kipping (2013). We fixed the eccentricity to 0, given that the expected time of circularization is 50 Myr (assuming a quality factor Qp ∼ 500, Goldreich & Soter 1966; Patra et al. 2017), which is at least an order of magnitude smaller than the estimated age of the system. For K2 data, we supersample the transits by a factor of 15 in batman to take into account the effect of non-negligible integration time. As for the ground-based data, we use second-order polynomials to detrend against the observables airmass and FWHM. We ran the MCMC for 50,000 steps with 150 walkers performing a combined fit of K2 and SPECULOOS — (1) This work. (2) Huber et al. (2016). (3) Stassun et al. (2019). (4) Cutri et al. (2003). (5) Gaia Collaboration et al. (2018). (6) Cutri et al. (2013). (7) This work, evolutionary model analysis. (8) This work, Keck/HIRES analysis. (9) This work, SED analysis. References data, and use the last half of the run to build the parameter posteriors. We assessed the convergence of walkers using the suggested autocorrelation test for emcee. The resulting median values from the fit with 1σ deviation are reported in Table 2, while the best-fit transit models are shown in Figure 2 and Figure 3. [2] http://kinematics.bdnyc.org/query [3] http://stdatu.stsci.edu/cgi-bin/dss form

π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Analysis and Validation

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 2. OBSERVATIONS 2.1. A Candidate in Archival K2 Data EPIC 249631677 was observed by K2 in Campaign 15 from 2017-08-23 22:18:11 UTC to 2017-11-19 22:58:27 UTC continuously for about 90 days as part of program GO 15005 (PI: I. Crossfield). The pointing was maintained by using two functioning reaction wheels, while the telescope drifted slowly in the third axis due to radiation pressure from the Sun, which was corrected periodically by thruster firing (Howell et al. 2014). As a consequence of such a modus operandi, uncorrected K2 light curves can show saw-tooth structures. Many pipelines have been built to correct for such systematics. Two popular detrending algorithms for K2 light curves are K2SFF (Vanderburg & Johnson 2014) and everest (Luger et al. 2016). These pipelines have helped to achieve precision comparable to that of Kepler by correcting for systematics caused by intrapixel and inter-pixel variations. In the case of EPIC 249631677b, the standard deviation of the flattened light curve for K2SFF was observed to be 1230 ppm, compared to 685 ppm for everest. Considering this, we use the light curve from the everest pipeline, available from the Mikulski Archive for Space Telescopes, throughout this analysis. We use a biweight filter with a window of 0.75 days, as implemented in w¯otan (Hippke et al. 2019), to generate the flattened light curve for further analysis, and use only data with quality factor of 0. This light curve can be seen in Figure 1. The simple aperture photometric light curve has a scatter of 2527 ppm, which improves to 685 ppm after everest processing. We searched the flattened data for periodic transit signals using the transit least squares algorithm (TLS) (Hippke & Heller 2019), and found a prominent peak around 3.14 days as can be seen in Figure 2. We assessed the presence of additional candidate signals after modeling out the 3.14-d signal by re-running TLS, but did not find any with a significant signal detection efficiency (i.e., SDE>10). 2.2. Candidate Vetting with SPECULOOS Telescopes We followed up on the planetary candidate by observing with SPECULOOS Southern Observatory (SSO) two transit windows on UT 25 February 2020 by Ganymede and on UT 18 March 2020 by Io, and one transit window with SPECULOOS Northern Observa-. tory on UT 18 May 2020 by Artemis. SSO is composed of four telescopes: Io, Europa, Ganymede and Callisto, which are installed at ESO Paranal Observatory (Chile) and have been operational since January 2018. SNO is currently composed of one telescope (Artemis), which is located at the Teide Observatory (Canary Islands, Spain) and operational since June 2019. All SPECULOOS telescopes are identical robotic Ritchey-Chretien (F/8) telescopes with an aperture of 1-m. They are equipped with Andor iKon-L cameras with e2v 2K × 2K deep-depletion CCDs, which provide a Field of View (FoV) of 12 0 × 12 0 and the corresponding pixel scale is 0.35 00 pixel−1 (Delrez et al. 2018; Jehin et al. 2018). To schedule those windows we used the SPeculoos Observatory sChedule maKer (SPOCK), described in Sebastian et al. (in prep.). Observations were made with an exposure time of 40 s in an I+z filter, a custom filter (transmittance >90% from 750 nm to beyond 1000 nm) designed for the observation of faint red targets usually observed by the SPECULOOS survey (Delrez et al. 2018; Murray et al. 2020). SSO data were then processed using the SSO Pipeline, which accounts for the water vapor effects known to be significant for differential photometry of redder hosts with bluer comparison stars (Murray et al. 2020). SNO data were processed using prose, a Python-based data reduction package, which generates light curves from raw images (Garcia et al. in prep.). It creates a stacked image to extract the positions of the stars in the field, and uses the positions to perform aperture annulus photometry. A differential light curve is produced using a weighted light curve derived from the field stars, while the instrumental systematics, such as pointing shift and full width half maximum, are recorded to assist later in detrending. We show detrended light curves from SPECULOOS in Figure 3, where we recovered the transit events within 1σ of the calculated ephemeris from K2 data. Since these observations were obtained two years after K2 Campaign 15, they improve the precision of the transit ephemeris by an order of magnitude.

π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Observation

is knocking on the door, promising automated rollouts and stress-free sleep. But is manual deployment truly doomed? Dive into our post to uncover CI/CD's exciting future (and why you should hop on board before it zooms past)! CI/CD Enter the game-changer of the Dev world: . It's the automated superhero of deployments, swooping in to save us from error-prone manual processes and ushering in a new era of efficiency and quality. Continuous Integration and Continuous Delivery (CI/CD) So, is manual deployment officially a dinosaur? Let's crack open the dev code vault and see what the future holds. The Manual Monster: A Tale of Bugs and Broken Dreams Remember the stress of meticulously uploading code, tweaking configurations, and hoping for the best? Manual deployments were like tightrope walks over a pit of bugs, a nerve-wracking dance with potential disaster. And don't even get me started on the "rollbacks of doom" when something goes wrong, sending you back into the code trenches like a digital warthog. CI/CD to the Rescue: Your Code's BFF Picture this: your code undergoes a series of automated tests and checks every time you commit a change. Bugs get squashed on sight, like ants at a picnic. Then, your code seamlessly sails through the build, integration, and deployment stages, landing on servers faster than a cheetah on Red Bull. That's the magic of CI/CD – a streamlined, automated pipeline that takes care of everything, from code whispers to deployment roars. Manual Deployment vs. CI/CD Feature Manual Deployment CI/CD Process Manual, often ad-hoc Automated, continuous process Error Prone High risk of human error and inconsistencies Reduces errors, improves reliability Deployment Speed Slow, prone to delays and bottlenecks Fast, frequent deployments with shorter feedback loops Scalability Difficult to scale with larger codebases and teams Seamlessly scales with increased complexity Security Security measures may be implemented inconsistently Consistent security checks and automated deployments Team Collaboration Can lead to silos and communication breakdowns Encourages collaboration and shared responsibility Overall Efficiency Time-consuming and inefficient Improves productivity and resource allocation Why You Should Care (Beyond Avoiding Dev-Induced Hair Loss): CI/CD cuts down deployment times from hours to minutes, meaning new features and bug fixes reach users faster than ever. Think happy customers and a competitive edge sharper than a samurai sword. Speed Demon: The constant testing and automation lead to fewer errors and a more stable, polished product. Imagine fewer crashes than a space mission and smoother user experiences than a freshly paved road. Quality Control Ninja: CI/CD frees you from the tedium of manual deployments, letting you focus on what you do best – writing awesome code and crafting innovative solutions. Dev Workflow Wonderland: CI/CD integrates seamlessly with version control systems and collaboration tools, fostering better communication and teamwork among developers. We're talking a symphony of code commits, not a chaotic cacophony. Collaboration Champion: Benefits of CI/CD for Your Business Benefit Impact Deploy updates and features more frequently, get ahead of the competition. Faster Time to Market: Increased revenue, improved customer satisfaction Automate tasks, eliminate manual errors, and optimize resource utilization. Reduced Costs: Lower operation costs, higher ROI Catch bugs early and often, deliver higher quality software consistently. Improved Software Quality: Enhanced brand reputation, reduced customer support needs Free developers from manual tasks, focus on innovation and core development. Increased Team Productivity: Improved morale, higher employee engagement Adapt to changing requirements and market needs quickly and efficiently. Greater Scalability and Agility: Increased future-proofness, competitive advantage The Future of CI/CD: Beyond Deployments and Pizza Parties CI/CDs are not just about pushing code anymore. It's evolving into a strategic partner, helping us monitor performance, analyze data, and optimize our entire software delivery process. Think self-healing deployments, automated infrastructure provisioning, and AI-powered insights that make your code scream, "Shazam!" with intelligence. So, Is Manual Deployment Truly Dead? Well, not entirely. Like cassette tapes and landlines, it might still exist in some corners of the Dev world. But its time is waning, replaced by the smooth, efficient hum of CI/CD. Embrace it, learn it, and watch your development process soar to new heights. Trust me, your code (and your sanity) will thank you. Ready to Join the CI/CD Revolution? Here are some key takeaways: Don't try to tackle Everest on your first climb. Begin by automating simple tasks and gradually integrating CI/CD into your workflow. Start small: There's a plethora of CI/CD tools out there, each with its strengths and weaknesses. Do your research and find the ones that fit your needs and budget. Choose the right tools: Don't go rogue developer on this one. Get your team onboard, share best practices, and create a culture of CI/CD awareness. Communicate and collaborate: CI/CD is a journey, not a destination. Stay updated on the latest trends and tools, and continuously optimize your pipeline for maximum efficiency. Learn and adapt: Remember, the future of software development is automated, efficient, and powered by CI/CD. So, ditch the deployment drama and embrace the future. Your code, your team, and your sanity will be forever grateful. Share your thoughts on CI/CD in the comments below! What are your experiences with manual deployments? How has CI/CD impacted your workflow? Let's start a conversation and learn from each other!

EVERNEST

evernest.co

-- employees

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EVERNEST

evernest.co

-- employees

Since n.d.

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