Reconstruction of the transit signal in the presence of stellar variability

TL;DR

This paper addresses the challenge of stellar variability in space-based transit data, proposing an iterative filtering method that improves the accuracy of planet parameter estimation by better reconstructing transit signals.

Contribution

It introduces a novel iterative filter leveraging transit periodicity to separate signals from stellar variability, enhancing transit shape recovery.

Findings

Improved estimation of transit depth by 15%.

Enhanced accuracy of transit duration measurement by 10%.

Effective separation of stellar variability from transit signals.

Abstract

Intrinsic stellar variability can hinder the detection of shallow transits, particularly in space-based data. Therefore, this variability has to be filtered out before running the transit search. Unfortunately, filtering out the low frequency signal of the stellar variability also modifies the transit shape. This results in errors in the measured transit depth and duration used to derive the planet radius, and orbital inclination. We present an evaluation of the magnitude of this effect based on 20 simulated light curves from the CoRoT blind exercise 2 (BT2). We then present an iterative filter which uses the strictly periodic nature of the transits to separate them from other forms of variability, so as to recover the original transit shape before deriving the planet parameters. On average with this filter, we improve the estimation of the transit depth and duration by 15% and 10% respectively.