Linking Anomalous Behaviour with Stellar Properties: An Unsupervised Exploration of TESS Light Curves

TL;DR

This study employs an unsupervised machine learning approach to identify and analyze anomalous stellar light curves from TESS data, revealing diverse astrophysical phenomena and differences from Kepler anomalies.

Contribution

It introduces an unsupervised random forest method for anomaly detection in TESS light curves and links anomalies to stellar properties and evolutionary stages.

Findings

Identified various anomalous light curves including rapid pulsators and eclipsing binaries.

Found differences in anomaly types between Kepler and TESS datasets.

Linked anomalies to specific physical parameters and stellar configurations.

Abstract

With the upcoming plethora of astronomical time-domain datasets and surveys, anomaly detection as a way to discover new types of variable stars and transients has inspired a new wave of research. Yet, the fundamental definition of what constitutes an anomaly and how this depends on the overall properties of the population of light curves studied remains a discussed issue. Building on a previous study focused on Kepler light curves, we present an analysis that uses the Unsupervised Random Forest to search for anomalies in TESS light curves. We provide a catalogue of anomalous light curves, classify them according to their variability characteristics and associate their anomalous nature to any particular evolutionary stage or astrophysical configuration. For anomalies belonging to known classes (e.g. eclipsing binaries), we have investigated which physical parameters drive the anomaly score. We find a combination of unclassified anomalies and objects of a known class with outlying physical configurations, such as rapid pulsators, deep eclipsing binaries of long periods, and irregular light curves due to obscuration in YSOs. Remarkably, we find that the set of anomalous types differ between the Kepler and TESS datasets, indicating that the overall properties of the parent population are an important driver of anomalous behaviour.