EVEREST: Pixel Level Decorrelation of K2 Light curves

TL;DR

EVEREST is an open-source pipeline that significantly improves the precision of K2 light curves by removing instrumental noise, aiding exoplanet detection and stellar studies, and can be adapted for future missions like TESS.

Contribution

We introduce EVEREST, a novel pixel level decorrelation pipeline combined with Gaussian processes for enhanced noise removal in K2 light curves, outperforming existing methods.

Findings

Achieves the highest average precision for unsaturated K2 stars.

Provides light curves with Kepler-like precision for bright stars.

Easily adaptable for future surveys like TESS.

Abstract

We present EVEREST, an open-source pipeline for removing instrumental noise from K2 light curves. EVEREST employs a variant of pixel level decorrelation (PLD) to remove systematics introduced by the spacecraft's pointing error and a Gaussian process (GP) to capture astrophysical variability. We apply EVEREST to all K2 targets in campaigns 0-7, yielding light curves with precision comparable to that of the original Kepler mission for stars brighter than $K_p \approx 13$, and within a factor of two of the Kepler precision for fainter targets. We perform cross-validation and transit injection and recovery tests to validate the pipeline, and compare our light curves to the other de-trended light curves available for download at the MAST High Level Science Products archive. We find that EVEREST achieves the highest average precision of any of these pipelines for unsaturated K2 stars. The improved precision of these light curves will aid in exoplanet detection and characterization, investigations of stellar variability, asteroseismology, and other photometric studies. The EVEREST pipeline can also easily be applied to future surveys, such as the TESS mission, to correct for instrumental systematics and enable the detection of low signal-to-noise transiting exoplanets. The EVEREST light curves and the source code used to generate them are freely available online.