Detecting Tidal Features using Self-Supervised Representation Learning

TL;DR

This paper introduces a self-supervised machine learning approach that significantly improves the detection of faint tidal features around galaxies, outperforming previous methods in completeness while maintaining low contamination.

Contribution

The paper presents a novel self-supervised model trained on deep survey data that enhances automated detection of tidal features beyond prior supervised techniques.

Findings

Achieves 96% completeness at 22% contamination

Outperforms previous automated detection methods

Demonstrates effectiveness of self-supervised learning in astrophysical image analysis

Abstract

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.