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

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 like the Trend Filtering Algorithm (TFA) use simultaneously-observed stars to remove systematic effects, and binning is used to reduce high-frequency random noise. We present TFAW, a wavelet-based modified version of TFA. TFAW aims to increase the periodic signal detection and to return a detrended and denoised signal without modifying its intrinsic characteristics. We modify 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 filter is added to TFA's signal reconstruction to perform an adaptive characterization of the noise- and trend-free signal and the 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, improving their characterization. TFAW improves the signal detection rate by increasing the signal detection efficiency (SDE) up to a factor ~2.5x for low SNR light curves. For simulated transits, the transit detection rate improves by a factor ~2-5x in the low-SNR regime compared to TFA. TFAW signal approximation performs up to a factor ~2x better than bin averaging for planetary transits. The standard deviations of simulated and real TFAW light curves are ~40x better than TFA. TFAW yields better MCMC posterior distributions and returns lower uncertainties, less biased transit parameters and narrower (~10x) credibility intervals for simulated transits. We present a newly-discovered variable star from TFRM.Comment: Accepted for publication by A&A. 13 pages, 16 figures and 5 table

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

Optical microflares in LS I +61 303 and the search for their multiwavelength counterpart

Stellar sources of gamma rays are one of the front lines in modern astrophysics whose understanding can benefit from observational tools not originally designed for their study. We take advantage of the high precision photometric capabilities of present-day space facilities to obtain a new perspective on the optical behavior of the X-ray and gamma-ray binary LS I +61 303. Previously unknown phenomena whose effects manifest with amplitudes below 0.01 magnitude can now be clearly observed and studied. Our work is mainly based on the analysis of optical and gamma-ray archival data and uses the tools recommended by the different collaborations that provide these valuable observational resources (in particular, the TESS and Fermi orbiting observatories). In addition, complementary ground-based optical spectroscopy has also been conducted. We report the discovery of small-amplitude optical flares on timescales of a day in the LS I +61 303 light curve. Different alternative scenarios to explain their origin are tentatively proposed.Comment: In press in Astronomy & Astrophysics. 6 pages, 5 figure