TFAW survey - I. Wavelet-based denoising of K2 light curves. Discovery and validation of two new Earth-sized planets in K2 campaign 1

The wavelet-based detrending and denoising method TFAW is applied for the first time to EVEREST 2.0-corrected light curves to further improve the photometric precision of almost all K2 observing campaigns (C1-C8, C12-C18). The performance of both methods is evaluated in terms of 6 h combined differential photometric precision (CDPP), simulated transit detection efficiency, and planet characterization in different SNR regimes. On average, TFAW median 6 h CDPP is ∼30percent better than the one achieved by EVEREST 2.0 for all observing campaigns. Using the TRANSIT LEAST-SQUARES (TLS) algorithm, we show that the transit detection efficiency for simulated Earth-Sun-like systems is ∼8.5× higher for TFAW-corrected light curves than that for EVEREST 2.0 ones. Using the light curves of two confirmed exoplanets, K2-44 b (high SNR) and K2-298 b (low SNR), we show that TFAW yields better Markov chain Monte Carlo posterior distributions, transit parameters compatible with the catalogued ones but with smaller uncertainties, and narrows the credibility intervals. We use the combination of TFAW's improved photometric precision and TLS enhancement of the signal detection efficiency for weak signals to search for new transit candidates in K2 observing campaign 1. We report the discovery of two new K2-C1 Earth-sized planets statistically validated, using the VESPA software: EPIC 201170410.02, with a radius of 1.047+0.276−0.257R⊕ planet orbiting an M-type star, and EPIC 201757695.02, with a radius of 0.908+0.059−0.064R⊕ planet orbiting a K-type star. EPIC 201757695.02 is the 9th smallest planet ever discovered in K2-C1, and the 39th smallest in all K2 campaigns

TFAW survey. I. Wavelet-based denoising of K2 light curves. Discovery and validation of two new Earth-sized planets in K2 campaign 1

The wavelet-based detrending and denoising method \texttt{TFAW} is applied for the first time to \texttt{EVEREST 2.0}-corrected light curves to further improve the photometric precision of almost all K2 observing campaigns (C1-C8, C12-C18). The performance of both methods is evaluated in terms of 6 hr combined differential photometric precision (CDPP), simulated transit detection efficiency, and planet characterization in different SNR regimes. On average, \texttt{TFAW} median 6hr CDPP is $\sim$30$\%$ better than the one achieved by \texttt{EVEREST 2.0} for all observing campaigns. Using the \texttt{transit least-squares} (\texttt{TLS}) algorithm, we show that the transit detection efficiency for simulated Earth-Sun-like systems is $\sim$8.5$\times$ higher for \texttt{TFAW}-corrected light curves than for \texttt{EVEREST 2.0} ones. Using the light curves of two confirmed exoplanets, K2-44 b (high-SNR) and K2-298 b (low-SNR), we show that \texttt{TFAW} yields better MCMC posterior distributions, transit parameters compatible with the cataloged ones but with smaller uncertainties and narrows the credibility intervals. We use the combination of \texttt{TFAW}'s improved photometric precision and \texttt{TLS} enhancement of the signal detection efficiency for weak signals to search for new transit candidates in K2 observing campaign 1. We report the discovery of two new K2-C1 Earth-sized planets statistically validated, using the \texttt{vespa} software: EPIC 201170410.02, with a radius of 1.047$^{+0.276}_{-0.257}R_{\oplus}$ planet orbiting an M-type star, and EPIC 201757695.02, with a radius of 0.908$^{+0.059}_{-0.064}R_{\oplus}$ planet orbiting a K-type star. EPIC 201757695.02 is the 9-th smallest planet ever discovered in K2-C1, and the 39-th smallest in all K2 campaigns.Comment: 22 pages, 11 figures, 10 tables. Published in MNRA

General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them. The term General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. Whilst we might still be far from achieving that, GPAIS is a reality and sitting at the forefront of AI research. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations. We distinguish between closed-world and open-world GPAIS, characterising their degree of autonomy and ability based on several factors such as adaptation to new tasks, competence in domains not intentionally trained for, ability to learn from few data, or proactive acknowledgment of their own limitations. We then propose a taxonomy of approaches to realise GPAIS, describing research trends such as the use of AI techniques to improve another AI or foundation models. As a prime example, we delve into generative AI, aligning them with the terms and concepts presented in the taxonomy. Through the proposed definition and taxonomy, our aim is to facilitate research collaboration across different areas that are tackling general-purpose tasks, as they share many common aspects. Finally, we discuss the current state of GPAIS, its challenges and prospects, implications for our society, and the need for responsible and trustworthy AI systems and regulation, with the goal of providing a holistic view of GPAIS

Building the Evryscope: Hardware Design and Performance

The Evryscope is a telescope array designed to open a new parameter space in optical astronomy, detecting short timescale events across extremely large sky areas simultaneously. The system consists of a 780 MPix 22-camera array with an 8150 sq. deg. field of view, 13" per pixel sampling, and the ability to detect objects down to Mg=16 in each 2 minute dark-sky exposure. The Evryscope, covering 18,400 sq.deg. with hours of high-cadence exposure time each night, is designed to find the rare events that require all-sky monitoring, including transiting exoplanets around exotic stars like white dwarfs and hot subdwarfs, stellar activity of all types within our galaxy, nearby supernovae, and other transient events such as gamma ray bursts and gravitational-wave electromagnetic counterparts. The system averages 5000 images per night with ~300,000 sources per image, and to date has taken over 3.0M images, totaling 250TB of raw data. The resulting light curve database has light curves for 9.3M targets, averaging 32,600 epochs per target through 2018. This paper summarizes the hardware and performance of the Evryscope, including the lessons learned during telescope design, electronics design, a procedure for the precision polar alignment of mounts for Evryscope-like systems, robotic control and operations, and safety and performance-optimization systems. We measure the on-sky performance of the Evryscope, discuss its data-analysis pipelines, and present some example variable star and eclipsing binary discoveries from the telescope. We also discuss new discoveries of very rare objects including 2 hot subdwarf eclipsing binaries with late M-dwarf secondaries (HW Vir systems), 2 white dwarf / hot subdwarf short-period binaries, and 4 hot subdwarf reflection binaries. We conclude with the status of our transit surveys, M-dwarf flare survey, and transient detection.Comment: 24 pages, 24 figures, accepted PAS

TFAW: Wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys

Context. There have been many efforts to correct systematic effects in astronomical light curves to improve the detection and characterization of planetary transits and astrophysical variability. Algorithms such as the trend filtering algorithm (TFA) use simultaneously-observed stars to measure and remove systematic effects, and binning is used to reduce high-frequency random noise. Aims: We present TFAW, a wavelet-based modified version of TFA. First, TFAW aims to increase the periodic signal detection and second, to return a detrended and denoised signal without modifying its intrinsic characteristics. Methods: We modified TFA's frequency analysis step adding a stationary wavelet transform filter to perform an initial noise and outlier removal and increase the detection of variable signals. A wavelet-based filter was added to TFA's signal reconstruction to perform an adaptive characterization of the noise- and trend-free signal and the underlying noise contribution at each iteration while preserving astrophysical signals. We carried out tests over simulated sinusoidal and transit-like signals to assess the effectiveness of the method and applied TFAW to real light curves from TFRM. We also studied TFAW's application to simulated multiperiodic signals. Results: TFAW improves the signal detection rate by increasing the signal detection efficiency (SDE) up to a factor ̃2.5× for low S/R light curves. For simulated transits, the transit detection rate improves by a factor ̃2 - 5× in the low-S/R regime compared to TFA. TFAW signal approximation performs up to a factor ̃2× better than bin averaging for planetary transits. The standard deviations of simulated and real TFAW light curves are ̃40% better compared to TFA. TFAW yields better MCMC posterior distributions and returns lower uncertainties, less biased transit parameters and narrower (by approximately ten times) credibility intervals for simulated transits. TFAW is also able to improve the characterization of multiperiodic signals. We present a newly-discovered variable star from TFRM

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.This work was supported by grants from the Spanish Ministry of Science (TIN2016-8113-R, TIN2017-89517-P and TIN2017-83132-C2- 2-R) and Universidad Politécnica de Madrid (PINV-18-XEOGHQ-19- 4QTEBP). Eneko Osaba and Javier Del Ser-would also like to thank the Basque Government for its funding support through the ELKARTEK and EMAITEK programs. Javier Del Ser-receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government

EvryFlare II: Rotation Periods of the Cool Flare Stars in TESS Across Half the Southern Sky

We measure rotation periods and sinusoidal amplitudes in Evryscope light curves for 122 two-minute K5-M4 TESS targets selected for strong flaring. The Evryscope array of telescopes has observed all bright nearby stars in the South, producing two-minute cadence light curves since 2016. Long-term, high-cadence observations of rotating flare stars probe the complex relationship between stellar rotation, starspots, and superflares. We detect periods from 0.3487 to 104 d, and observe amplitudes from 0.008 to 0.216 g' mag. We find the Evryscope amplitudes are larger than those in TESS with the effect correlated to stellar mass (p-value=0.01). We compute the Rossby number (Ro), and find our sample selected for flaring has twice as many intermediate rotators (0.040.44) rotators; this may be astrophysical or a result of period-detection sensitivity. We discover 30 fast, 59 intermediate, and 33 slow rotators. We measure a median starspot coverage of 13% of the stellar hemisphere and constrain the minimum magnetic field strength consistent with our flare energies and spot coverage to be 500 G, with later-type stars exhibiting lower values than earlier-types. We observe a possible change in superflare rates at intermediate periods. However, we do not conclusively confirm the increased activity of intermediate rotators seen in previous studies. We split all rotators at Ro~0.2 into Prot10 d bins to confirm short-period rotators exhibit higher superflare rates, larger flare energies, and higher starspot coverage than do long-period rotators, at p-values of 3.2 X 10^-5, 1.0 X 10^-5, and 0.01, respectively.Comment: 16 pages, 8 figures, 3 tables. Ancillary machine-readable files included. Accepted for publication in ApJ (proofs submitted). Includes significant new material, including starspot color that depends on stellar mass, more rotation periods, potential changes in activity during spin-down, and examples of binary rotator

The Robotilter: An Automated Lens / CCD Alignment System for the Evryscope

Camera lenses are increasingly used in wide-field astronomical surveys due to their high performance, wide field-of-view (FOV) unreachable from traditional telescope optics, and modest cost. The machining and assembly tolerances for commercially available optical systems cause a slight misalignment (tilt) between the lens and CCD, resulting in PSF degradation. We have built an automated alignment system (Robotilters) to solve this challenge, optimizing 4 degrees of freedom - 2 tilt axes, a separation axis (the distance between the CCD and lens), and the lens focus (the built-in focus of the lens by turning the lens focusing ring which moves the optical elements relative to one another) in a compact and low-cost package. The Robotilters remove tilt and optimize focus at the sub 10 micron level, are completely automated, take 2 hours to run, and remain stable for multiple years once aligned. The Robotilters were built for the Evryscope telescope (a 780 MPix 22-camera array with an 8150 sq.deg. field of view and continuous 2-minute cadence) designed to detect short timescale events across extremely large sky areas simultaneously. Variance in quality across the image field, especially the corners and edges compared to the center, is a significant challenge in wide-field astronomical surveys like the Evryscope. The individual star PSFs (which typically extend only a few pixels) are highly susceptible to slight increases in optical aberrations in this situation. The Robotilter solution resulted in a limiting magnitude improvement of .5 mag in the center of the image and 1.0 mag in the corners for typical Evryscope cameras, with less distorted and smaller PSFs (half the extent in the corners and edges in many cases). In this paper we describe the Robotilter mechanical and software design, camera alignment results, long term stability, and image improvement.Comment: Accepted to JATIS, January 202

Variables in the Southern Polar Region Evryscope 2016 Dataset

The regions around the celestial poles offer the ability to find and characterize long-term variables from ground-based observatories. We used multi-year Evryscope data to search for high-amplitude (~5% or greater) variable objects among 160,000 bright stars (Mv < 14.5) near the South Celestial Pole. We developed a machine learning based spectral classifier to identify eclipse and transit candidates with M-dwarf or K-dwarf host stars - and potential low-mass secondary stars or gas giant planets. The large amplitude transit signals from low-mass companions of smaller dwarf host stars lessens the photometric precision and systematics removal requirements necessary for detection, and increases the discoveries from long-term observations with modest light curve precision. The Evryscope is a robotic telescope array that observes the Southern sky continuously at 2-minute cadence, searching for stellar variability, transients, transits around exotic stars and other observationally challenging astrophysical variables. In this study, covering all stars 9 < Mv < 14.5, in declinations -75 to -90 deg, we recover 346 known variables and discover 303 new variables, including 168 eclipsing binaries. We characterize the discoveries and provide the amplitudes, periods, and variability type. A 1.7 Jupiter radius planet candidate with a late K-dwarf primary was found and the transit signal was verified with the PROMPT telescope network. Further followup revealed this object to be a likely grazing eclipsing binary system with nearly identical primary and secondary K5 stars. Radial velocity measurements from the Goodman Spectrograph on the 4.1 meter SOAR telescope of the likely-lowest-mass targets reveal that six of the eclipsing binary discoveries are low-mass (.06 - .37 solar mass) secondaries with K-dwarf primaries, strong candidates for precision mass-radius measurements.Comment: 32 pages, 17 figures, accepted to PAS