The GALAH survey: Chemical Tagging of Star Clusters and New Members in the Pleiades

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 tagging because it can: (i) resolve clustering in chemical space alone, (ii) recover known open and globular clusters with high efficiency and low contamination, and (iii) relate field stars to known clusters. t-SNE also provides a useful visualization of a high-dimensional space. We demonstrate the method on a dataset of 13 abundances measured in the spectra of 187,000 stars by the GALAH survey. We recover 7 of the 9 observed clusters (6 globular and 3 open clusters) in chemical space with minimal contamination from field stars and low numbers of outliers. With chemical tagging, we also identify two Pleiades supercluster members (which we confirm kinematically), one as far as 6${}^{\circ }$ -- one tidal radius away from the cluster centre.

References in corpus (15)

• The GALAH Survey: Scientific Motivation
• The GALAH Survey: Observational Overview and Gaia DR1 companion
• Chemical Homogeneity in Collinder 261 and Implications for Chemical Tagging
• Bayesian Ages for Early-Type Stars from Isochrones Including Rotation, and a Possible Old Age for the Hyades
• Early turbulent mixing as the origin of chemical homogeneity in open star clusters
• The K2 Galactic Archaeology Program Data Release 1: Asteroseismic results from Campaign 1
• The GALAH survey: The data reduction pipeline
• The K2 M67 Study: Revisiting old friends with K2 reveals oscillating red giants in the open cluster M67
• The Galah Survey: Classification and diagnostics with t-SNE reduction of spectral information
• Chemical composition of A and F dwarfs members of the Pleiades open cluster
• Testing the chemical tagging technique with open clusters
• Prospects for chemically tagging stars in the Galaxy
• The chemical composition of red giants in 47 Tucanae I: Fundamental parameters and chemical abundance patterns
• Element abundances in the metal rich open cluster NGC6253
• Cosmic phylogeny: reconstructing the chemical history of the solar neighbourhood with an evolutionary tree

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• Open clusters in APOGEE and GALAH: Combining Gaia and ground-based spectroscopic surveys
• How Many Elements Matter?
• The (im)possibility of strong chemical tagging
• Stellar Characterization of Keck HIRES Spectra with The Cannon
• Five young $\delta$ Scuti stars in the Pleiades seen with Kepler/K2
• The GALAH Survey: Chemically tagging the Fimbulthul stream to the globular cluster $\omega$ Centauri
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• Classification of High-resolution Solar Hα Spectra using t-distributed Stochastic Neighbor Embedding
• Tidal debris from Omega Centauri discovered with unsupervised machine learning
• K2-HERMES II. Planet-candidate properties from K2 Campaigns 1-13
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• The GALAH Survey: A New Sample of Extremely Metal-Poor Stars Using A Machine Learning Classification Algorithm
• Deep chemical tagging -- Identifying open clusters and moving groups in chemical space with graph attention networks
• Searching for and characterizing halo substructures with the GALAH DR4 survey