The GALAH survey: Chemical Tagging of Star Clusters and New Members in the Pleiades
Janez Kos, Joss Bland-Hawthorn, Ken Freeman, Sven Buder, Gregor, Traven, Gayandhi M. De Silva, Sanjib Sharma, Martin Asplund, Ly Duong, Jane, Lin, Karin Lind, Sarah Martell, Jeffrey D. Simpson, Dennis Stello, Daniel B., Zucker, Toma\v{z} Zwitter, Borja Anguiano, Gary Da Costa

TL;DR
This paper demonstrates that t-SNE is an effective method for chemical tagging in high-dimensional stellar abundance data, successfully recovering known star clusters and identifying new members in the GALAH survey.
Contribution
The study introduces the use of t-SNE for chemical tagging in stellar spectra, showing its reliability in identifying clusters and new members in large, noisy datasets.
Findings
Recovered 7 of 9 known clusters with high accuracy
Successfully identified Pleiades members, including a distant one
Demonstrated t-SNE's effectiveness in visualizing high-dimensional chemical space
Abstract
The technique of chemical tagging uses the elemental abundances of stellar atmospheres to `reconstruct' chemically homogeneous star clusters that have long since dispersed. The GALAH spectroscopic survey --which aims to observe one million stars using the Anglo-Australian Telescope -- allows us to measure up to 30 elements or dimensions in the stellar chemical abundance space, many of which are not independent. How to find clustering reliably in a noisy high-dimensional space is a difficult problem that remains largely unsolved. Here we explore t-distributed stochastic neighbour embedding (t-SNE) -- which identifies an optimal mapping of a high-dimensional space into fewer dimensions -- whilst conserving the original clustering information. Typically, the projection is made to a 2D space to aid recognition of clusters by eye. We show that this method is a reliable tool for chemical…
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