Chi-Square Discriminators for Transiting Planet Detection in Kepler Data

TL;DR

This paper introduces a chi-square test method, adapted from gravitational wave detection techniques, to reduce false alarms in Kepler's transiting planet data, significantly improving detection reliability.

Contribution

It presents a novel application of chi-square discriminators to Kepler data, enhancing false alarm mitigation in exoplanet detection pipelines.

Findings

False alarm rate reduced to approximately 5%

Method effectively discriminates true transits from false alarms

Improves overall detection sensitivity in Kepler data

Abstract

The Kepler spacecraft observes a host of target stars to detect transiting planets. Requiring a 7.1 sigma detection in twelve quarters of data yields over 100,000 detections, many of which are false alarms. After a second cut is made on a robust detection statistic, some 50,000 or more targets still remain. These false alarms waste resources as they propagate through the remainder of the software pipeline and so a method to discriminate against them is crucial in maintaining the desired sensitivity to true events. This paper describes a chi-square test which represents a novel application of the formalism developed by Allen for false alarm mitigation in searches for gravitational waves. Using this technique, the false alarm rate can be lowered to ~5%.