J/A+A/629/A34 APOGEE stars members of 35 star clusters (Garcia-Dias+, 2019)
Machine learning in APOGEE:
Identification of stellar populations through chemical abundances.
Garcia-Dias R., Allende Prieto C., Sanchez Almeida J., Alonso Palicio P.
<Astron. Astrophys. 629, A34 (2019)>
=2019A&A...629A..34G 2019A&A...629A..34G (SIMBAD/NED BibCode)
ADC_Keywords: Clusters, globular; Clusters, open
Keywords: open clusters and associations: general - globular clusters: general -
Galaxy: abundances - methods: numerical - methods: statistical -
methods: data analysis
Abstract:
The vast volume of data generated by modern astronomical surveys offer
test beds for the application of machine learning. In these
exploratory applications, it is important to evaluate potential
existing tools and determine which ones are optimal to extract
scientific knowledge from the available observations. This work aims
to explore the possibility of using unsupervised clustering algorithms
to separate stellar populations with distinct chemical patterns. Star
clusters are likely the most chemically homogeneous populations in the
Galaxy, and therefore any practical approach to identify distinct
stellar populations should at least be able to separate clusters from
each other. We have applied eight clustering algorithms combined with
four dimensionality reduction strategies to discriminate automatically
stellar clusters using chemical abundances of 13 elements. Our
test-bed sample includes 18 stellar clusters with a total of 453
stars. We have applied statistical tests showing that some pairs of
clusters (e.g., NGC 2458-NGC 2420) are indistinguishable from each
other when using the Apache Point Galactic Evolution Experiment
(APOGEE) chemical abundances. However, for most clusters we are able
to automatically assign membership with metric scores similar to
previous works. The confusion level of the automatically selected
clusters is consistent with statistical tests that demonstrate the
impossibility of perfectly discriminating all the clusters from each
other. These statistical tests, and confusion levels establish a limit
for the prospect of blindly identifying stars born in the same cluster
based solely on chemical abundances. We find that some of the
algorithms explored are capable of blindly identify stellar
populations with similar ages and chemical distributions in the APOGEE
data. Even though we are not able to fully separate the clusters from
each other, the main confusion arises from clusters with similar ages.
Since there are stellar clusters that are chemically
indistinguishable, our study supports the notion of extending weak
chemical tagging involving families of clusters instead of individual
clusters.
Description:
Initial list of stars used in the article.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 30 2326 Initial list of stars used in the article
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Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 18 A18 --- APOGEE ID from APOGEE survey (2MHHMMSSss+DDMMSSs)
20- 30 A11 --- Cluster Cluster for which the star is a member (1)
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Note (1): List of clusters:
Berkeley 17, Berkeley 29, Berkeley 53, Berkeley 66, Berkeley 71, FSR 0494,
IC 166, King 5, King 7, M 107, M 13, M 15, M 2, M 3, M 35, M 5, M 53, M 67,
M 71, M 92, NGC 1245, NGC 1798, NGC 188, NGC 2158, NGC 2243, NGC 2420,
NGC 2682, NGC 4147, NGC 5466, NGC 6791, NGC 6811, NGC 6819, NGC 7789,
Pleiades and Teutsch 51
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Acknowledgements:
Rafael Garcia-Dias, rafaelagd(at)gmail.com
(End) Rafael Garcia-Dias [London, UK], Patricia Vannier [CDS] 28-Jul-2019