TFAW survey. I. Wavelet-based denoising of K2 light curves. Discovery and validation of two new Earth-sized planets in K2 campaign 1
Daniel del Ser, Octavi Fors

TL;DR
This paper introduces TFAW, a wavelet-based denoising method that significantly improves photometric precision in K2 light curves, leading to better transit detection and the discovery of two new Earth-sized exoplanets in campaign 1.
Contribution
The paper presents TFAW, a novel wavelet-based denoising technique, and demonstrates its effectiveness in enhancing transit detection and characterization in K2 data, resulting in new planet discoveries.
Findings
TFAW improves 6hr CDPP by ~30% over EVEREST 2.0.
Transit detection efficiency increases by ~8.5× with TFAW.
Two new Earth-sized planets validated in K2 campaign 1.
Abstract
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 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 8.5 higher for \texttt{TFAW}-corrected light curves than for \texttt{EVEREST 2.0} ones. Using the light curves of two confirmed exoplanets,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
