Matched filtering with non-Gaussian noise for planet transit detections

TL;DR

This paper introduces a fast, Gaussianized matched filter technique for planet transit detection that outperforms standard methods in false positive reduction and accurately extracts transit parameters from data.

Contribution

The paper presents the Gaussianized Matched Filter (GMF), a novel method combining Fourier transforms and noise Gaussianization for improved planet detection and parameter estimation.

Findings

Significantly reduces false positive rate in planet detection.

Enables detection of transits with amplitudes up to 30% lower.

Accurately estimates all main transit parameters with minimal bias and variance.

Abstract

We develop a method for planet detection in transit data, which is based on the Matched Filter technique, combined with the Gaussianization of the noise outliers. The method is based on Fourier transforms and is as fast as the existing methods for planet searches. The Gaussinized Matched Filter (GMF) method significantly outperforms the standard baseline methods in terms of the false positive rate, enabling planet detections at up to 30 % lower transit amplitudes. Moreover, the method extracts all the main planet transit parameters, amplitude, period, phase, and duration. By comparison to the state of the art Gaussian Process methods on both simulations and real data we show that all the transit parameters are determined with an optimal accuracy (no bias and minimum variance), meaning that the GMF method can be used both for the initial planet detection and the follow-up planet parameter analysis.