The DOHA algorithm: a new recipe for cotrending large-scale transiting exoplanet survey light curves

TL;DR

DOHA is a novel cotrending algorithm for exoplanet survey light curves that improves data quality and transit detection probability by selecting optimal comparison stars through a two-step correlation method.

Contribution

It introduces a new comparison star selection approach for cotrending, enhancing correction of systematics in large-scale exoplanet survey data.

Findings

RMS noise reduced by a factor of 2 after correction.

Transit detection probability increased up to 7 times.

Effective correction of intra-night and long-term systematics.

Abstract

We present DOHA, a new algorithm for cotrending photometric light curves obtained by transiting exoplanet surveys. The algorithm employs a novel approach to the traditional "differential photometry" technique, by selecting the most suitable comparison star for each target light curve, using a two-step correlation search. Extensive tests on real data reveal that DOHA corrects both intra-night variations and long-term systematics affecting the data. Statistical studies conducted on a sample of 9500 light curves from the Qatar Exoplanet Survey reveal that DOHA-corrected light curves show an RMS improvement of a factor of 2, compared to the raw light curves. In addition, we show that the transit detection probability in our sample can increase considerably, even up to a factor of 7, after applying DOHA.