Exploration of groups and outliers in Gaia RVS stellar spectra with metric learning

TL;DR

This paper introduces a new dataset and interactive tool that uses metric learning and anomaly detection to help astronomers explore Gaia RVS stellar spectra, revealing groups and outliers efficiently.

Contribution

It presents a novel application of self-supervised metric learning and anomaly detection to Gaia RVS spectra, along with a public dataset and interactive portal for enhanced analysis.

Findings

Identification of stellar groups and outliers

Demonstration of interactive exploration capabilities

Potential discovery of new stellar phenomena

Abstract

The Gaia mission is transforming our view of the Milky Way by providing distances towards a billion stars, and much more. The third data release includes nearly a million spectra from its Radial Velocity Spectrometer (RVS). Identifying unexpected features in such vast datasets presents a significant challenge. It is impossible to visually inspect all of the spectra and difficult to analyze them in a comprehensive way. In order to supplement traditional analysis approaches, and in order to facilitate deeper insights from these spectra, we present a new dataset together with an interactive portal that applies established self-supervised metric learning techniques, dimensionality reduction, and anomaly detection, to allow researchers to visualize, analyze, and interact with the Gaia RVS spectra in straightforward but under-utilized manner. We demonstrate a few example interactions with the dataset, examining groupings and the most unusual RVS spectra, according to our metric. This combination of methodology and public availability enables broader exploration, and may reveal yet-to-be-discovered stellar phenomena.