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The GALAH survey : Chemical tagging of star clusters and new members in the Pleiades

Kos, Janez; Bland-Hawthorn, Joss; Freeman, Ken; Buder, Sven; Traven, Gregor LU ; De Silva, Gayandhi M.; Sharma, Sanjib; Asplund, Martin; Duong, Ly and Lin, Jane, et al. (2018) In Monthly Notices of the Royal Astronomical Society 473(4). p.4612-4633
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... (More)

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 data set of 13 abundances measured in the spectra of 187 000 stars by the GALAH survey. We recover seven of the nine observed clusters (six globular and three 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° - one tidal radius away from the cluster centre.

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published
subject
keywords
Methods: data analysis, Open clusters and associations: general, Open clusters and associations: individual: Pleiades, Stars: abundances
in
Monthly Notices of the Royal Astronomical Society
volume
473
issue
4
pages
22 pages
publisher
Oxford University Press (OUP)
external identifiers
  • scopus:85045770025
ISSN
0035-8711
DOI
10.1093/mnras/stx2637
language
English
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no
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080f0432-2539-4f30-8fbf-eff35b093a57
date added to LUP
2019-05-30 00:05:36
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2019-09-15 05:18:34
@article{080f0432-2539-4f30-8fbf-eff35b093a57,
  abstract     = {<p>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 data set of 13 abundances measured in the spectra of 187 000 stars by the GALAH survey. We recover seven of the nine observed clusters (six globular and three 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° - one tidal radius away from the cluster centre.</p>},
  author       = {Kos, Janez and Bland-Hawthorn, Joss and Freeman, Ken and Buder, Sven and Traven, Gregor and De Silva, Gayandhi M. and Sharma, Sanjib and Asplund, Martin and Duong, Ly and Lin, Jane and Lind, Karin and Martell, Sarah and Simpson, Jeffrey D. and Stello, Dennis and Zucker, Daniel B. and Zwitter, Tomaž and Anguiano, Borja and Costa, Gary Da and D'Orazi, Valentina and Horner, Jonathan and Kafle, Prajwal R. and Lewis, Geraint and Munari, Ulisse and Nataf, David M. and Ness, Melissa and Reid, Warren and Schlesinger, Katie and Ting, Yuan Sen and Wyse, Rosemary},
  issn         = {0035-8711},
  keyword      = {Methods: data analysis,Open clusters and associations: general,Open clusters and associations: individual: Pleiades,Stars: abundances},
  language     = {eng},
  month        = {01},
  number       = {4},
  pages        = {4612--4633},
  publisher    = {Oxford University Press (OUP)},
  series       = {Monthly Notices of the Royal Astronomical Society},
  title        = {The GALAH survey : Chemical tagging of star clusters and new members in the Pleiades},
  url          = {http://dx.doi.org/10.1093/mnras/stx2637},
  volume       = {473},
  year         = {2018},
}