arXiv subjects

D. del Ser

Publications and source records attributed to D. del Ser.

3 recordsLinked to original sources

arXiv2022

TFAW survey II: 6 Newly Validated Planets and 13 Planet Candidates from K2

Searching for Earth-sized planets in data from Kepler's extended mission (K2) is a niche that still remains to be fully exploited. The TFAW survey is an ongoing project that aims to re-analyze all light curves in K2 C1-C8 and C12-C18 campaigns with a wavelet-based detrending and denoising method, and the period search algorithm TLS to search for new transit candidates not detected in previous works. We have analyzed a first subset of 24 candidate planetary systems around relatively faint host stars (10.9 < $K_{p}$ < 15.4) to allow for follow-up speckle imaging observations. Using VESPA and TRICERATOPS, we statistically validate six candidates orbiting four unique host stars by obtaining false-positive probabilities smaller than 1% with both methods. We also present 13 vetted planet candidates that might benefit from other, more precise follow-up observations. All of these planets are sub-Neptune-sized, with two validated planets and three candidates with sub-Earth sizes, and have orbital periods between 0.81 and 23.98 days. Some interesting systems include two ultra-short-period planets, three multi-planetary systems, three sub-Neptunes that appear to be within the small planet Radius Gap, and two validated and one candidate sub-Earths (EPIC 210706310, EPIC 210768568, and EPIC 246078343) orbiting metal-poor stars.

astro-ph.EP↗

arXiv2018

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.

astro-ph.IM↗

arXiv2014

Detrending algorithms in large time-series: Application to TFRM-PSES data

Certain instrumental effects and data reduction anomalies introduce systematic errors in photometric time-series. Detrending algorithms such as the Trend Filtering Algorithm (TFA) (Kovács et al. 2004) have played a key role in minimizing the effects caused by these systematics. Here we present the results obtained after applying the TFA, Savitszky-Golay (Savitzky & Golay 1964) detrending algorithms and the Box Least Square phase folding algorithm (Kovács et al. 2002) to the TFRM-PSES data (Fors et al. 2013). Tests performed on this data show that by applying these two filtering methods together, the photometric RMS is on average improved by a factor of 3-4, with better efficiency towards brighter magnitudes, while applying TFA alone yields an improvement of a factor 1-2. As a result of this improvement, we are able to detect and analyze a large number of stars per TFRM-PSES field which present some kind of variability. Also, after porting these algorithms to Python and parallelizing them, we have improved, even for large data samples, the computing performance of the overall detrending+BLS algorithm by a factor of $\sim$10 with respect to Kovács et al. (2004).

astro-ph.IM↗