Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves

TL;DR

This paper introduces an improved Bayesian-based Presearch Data Conditioning module for Kepler light curves, effectively correcting systematic errors while preserving astrophysical signals, enhancing the detection of exoplanets and stellar activity.

Contribution

The paper presents a new PDC algorithm that significantly improves error correction in Kepler light curves without removing genuine astrophysical signals.

Findings

Effective correction of systematic errors in Kepler data.

Preservation of planetary transits during data processing.

Enhanced accuracy in stellar activity analysis.

Abstract

Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic trends, and outliers, obscure the astrophysical signals in the light curves. To correct these errors is the task of the Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline. The original version of PDC in Kepler did not meet the extremely high performance requirements for the detection of miniscule planet transits or highly accurate analysis of stellar activity and rotation. One particular deficiency was that astrophysical features were often removed as a side-effect to removal of errors. In this paper we introduce the completely new and significantly improved version of PDC which was implemented in Kepler SOC 8.0. This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals. We describe the architecture and the algorithms of this new PDC module, show typical errors encountered in Kepler data, and illustrate the corrections using real light curve examples.