The ones that got away: chemical tagging of globular cluster-origin stars with Gaia BP/RP spectra
Sarah G. Kane, Vasily Belokurov, Miles Cranmer, Stephanie Monty,, Hanyuan Zhang, Anke Ardern-Arentsen, Elana Kane

TL;DR
This study develops a neural network to identify stars originating from globular clusters using Gaia BP/RP spectra, successfully finding new GC candidates and providing insights into the Milky Way's early formation.
Contribution
The paper introduces a neural network method to detect GC-origin stars with unique chemical signatures from low-resolution Gaia spectra, achieving high accuracy and discovering new candidates.
Findings
Neural network classification accuracy of ~99%.
Identified 878 new GC-origin star candidates.
Most candidates are located in the inner Galaxy.
Abstract
Globular clusters (GCs) are sites of extremely efficient star formation, and recent studies suggest they significantly contributed to the early Milky Way's stellar mass build-up. Although their role has since diminished, GCs' impact on the Galaxy's initial evolution can be traced today by identifying their most chemically unique stars--those with anomalous nitrogen and aluminum overabundances and oxygen depletion. While they are a perfect tracer of clusters, be it intact or fully dissolved, these high-[N/O], high-[Al/Fe] GC-origin stars are extremely rare within the current Galaxy. To address the scarcity of these unusual, precious former GC members, we train a neural network (NN) to identify high-[N/O], high-[Al/Fe] stars using low-resolution Gaia BP/RP spectra. Our NN achieves a classification accuracy of approximately and a false positive rate of around ,…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Stellar, planetary, and galactic studies
