Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers. Their properties can answer questions about the progenitor galaxies involved in the interactions. This paper presents promising results from a self-supervised machine learning model, trained on data from the Ultradeep layer of the Hyper Suprime-Cam Subaru Strategic Program optical imaging survey, designed to automate the detection of tidal features. We find that self-supervised models are capable of detecting tidal features and that our model outperforms previous automated tidal feature detection methods, including a fully supervised model. The previous state of the art method achieved 76% completeness for 22% contamination, while our model achieves considerably higher (96%) completeness for the same level of contamination.
@article{arxiv.2307.04967,
title = {Detecting Tidal Features using Self-Supervised Representation Learning},
author = {Alice Desmons and Sarah Brough and Francois Lanusse},
journal= {arXiv preprint arXiv:2307.04967},
year = {2023}
}
Comments
Accepted at the ICML 2023 Workshop on Machine Learning for Astrophysics