DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

TL;DR

This paper presents a comprehensive pipeline combining ARIMA modeling, periodic signal detection, and machine learning classification to identify potential exoplanets from nearly one million TESS light curves, resulting in thousands of candidates.

Contribution

It introduces the DTARPS pipeline that integrates statistical modeling, signal detection, and machine learning to efficiently analyze large TESS datasets for exoplanet candidates.

Findings

Achieved a 92.8% true positive rate in candidate classification.

Generated a list of 7,377 potential exoplanets from TESS data.

Identified high recall for planets with 8-30 Earth radii and periods 0.6-13 days.

Abstract

Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends and lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. Classifier training sets include injections of both planetary transit signals and contaminating eclipsing binaries. The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. The completeness map of the injected planetary signals shows high recall rates for planets with 8 - 30 R(Earth) radii and periods 0.6-13 days and poor completeness for planets with radii < 2 R(Earth) or periods < 1 day. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).