K2SC: Flexible systematics correction and detrending ofK2light curves using Gaussian Process regression

Abstract

We present k2sc (K2 Systematics Correction), a Python pipeline to model instrumental systematics and astrophysical variability in light curves from the K2 mission. k2sc uses Gaussian process regression to model position-dependent systematics and time-dependent variability simultaneously, enabling the user to remove both (e.g., for transit searches) or to remove systematics while preserving variability (for variability studies). For periodic variables, k2sc automatically computes estimates of the period, amplitude and evolution timescale of the variability. We apply k2sc to publicly available K2 data from campaigns 3-5 showing that we obtain photometric precision approaching that of the original Kepler mission. We compare our results to other publicly available K2 pipelines, showing that we obtain similar or better results, on average. We use transit injection and recovery tests to evaluate the impact of k2sc on planetary transit searches in K2 pdc (Pre-search Data Conditioning) data, for planet-to-star radius ratio down R p /R = 0.01 and periods up to P = 40 d, and show that k2sc significantly improves the ability to distinguish between correct and false detections, particularly for small planets. k2sc can be run automatically on many light curves, or manually tailored for specific objects such as pulsating stars or large amplitude eclipsing binaries. It can be run on ASCII and FITS light curve files, regardless of their origin. Both the code and the processed light curves are publicly available, and we provide instructions for downloading and using them. The methodology used by k2sc will be applicable to future transit search missions such as TESS and PLATO.

Characterizing K2 Candidate Planetary Systems Orbiting Low-mass Stars. III. A High Mass and Low Envelope Fraction for the Warm Neptune K2-55b*

“…Next, we re-fit the transit parameters using photometry reduced with the K2SC pipeline (Aigrain et al 2016) and the k2phot pipeline (Petigura et al 2015). In both cases, we found consistent planet/star radius ratios of All of these values are in agreement with the previous estimate of R p /R å =0.0552±0.0013 (Crossfield et al 2016), which was based on fits to the k2phot photometry.…”

A Universal Spin–Mass Relation for Brown Dwarfs and Planets

“…We first pre-processed the PDCSAP light curves using K2SC (Aigrain et al 2016,https://github.com/OxES/k2sc) to remove pointing-related systematics while preserving astrophysical variability, then normalized each light curve by dividing it by its median. We then analyzed the systematicscorrected light curves using a quasi-periodic GP model, fitting for the period alongside the other parameters of the model.…”

K2-99: a subgiant hosting a transiting warm Jupiter in an eccentric orbit and a long-period companion

“…Independently, K2-99 was identified as a candidate by Aigrain et al (2016), which removes both instrumental and stellar noise. Portions of the light curve selected for modelling are shown in red (Section 3).…”

“…Pope et al (2016). Using the K2SC code of Aigrain et al (2016), which relies on Gaussian processes to correct simultaneously the light curve for K2 pointing systematics and stellar variability, Pope et al (2016) identified a total of 152 candidate transiting systems from K2 Campaigns 5 and 6. The K2SC light curve of K2-99 is shown in Fig.…”

“…The light curve of K2-99 we model is that generated by the K2SC code of Aigrain et al (2016) (see Section 2.1). We used only a subset of the light curve for modelling, selecting just over 1.5 times the transit duration both before and after each transit, such that the modelled light curve consists of four blocks of data, each around 2.2 d in duration, centred on each transit mid-point (see Fig.…”