J/A+A/672/A65 Clean SMC stellar samples (Jimenez-Arranz+, 2023)
Application of a neural network classifier for the generation of clean
Small Magellanic Cloud stellar samples.
Jimenez-Arranz O., Romero-Gomez M., Luri X., Masana E.
<Astron. Astrophys. 672, A65 (2023)>
=2023A&A...672A..65J 2023A&A...672A..65J (SIMBAD/NED BibCode)
ADC_Keywords: Magellanic Clouds ; Models
Keywords: Magellanic Clouds - astrometry - methods: data analysis
Abstract:
Previous attempts to separate Small Magellanic Cloud (SMC) stars from
the Milky Way (MW) foreground stars are based only on the proper
motions of the stars.
In this paper, we aim to develop a statistical classification
technique to effectively separate the SMC stars from the MW stars
using a wider set of Gaia data. We aim to reduce the possible
contamination from MW stars compared to previous strategies.
The new strategy is based on a neural network classifier, applied to
the bulk of the Gaia DR3 data. We produce three samples of stars
flagged as SMC members, with varying levels of completeness and
purity, obtained by application of this classifier. Using different
test samples, we validated these classification results and compared
them with the results of the selection technique employed in the Gaia
Collaboration papers, which was based solely on the proper motions.
The contamination of the MW in each of the three SMC samples is
estimated to be in the 10-40% range; the "best case" in this
range is obtained for bright stars (G<16), which belong to the V los
sub-samples, and the "worst case" for the full SMC sample
determined by using very stringent criteria based on StarHorse
distances. A further check based on the comparison with a nearby area
with uniform sky density indicates that the global contamination in
our samples is probably close to the low end of the range, around 10%.
We provide three selections of SMC star samples with different degrees
of purity and completeness, for which we estimate a low contamination
level and which we have successfully validated using SMC RR Lyrae, SMC
Cepheids, and SMC-MW StarHorse samples.
Description:
Table of the SMC/MW classification probability of each object in the
Gaia DR3 base sample. A Probability value close to 1 (0) means a high
probability of being a SMC (MW) star.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
class.dat 43 4047225 SMC/MW classification
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See also:
I/355 : Gaia DR3 Part 1. Main source (Gaia Collaboration, 2022)
Byte-by-byte Description of file: class.dat
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Bytes Format Units Label Explanations
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1- 19 I19 --- GaiaDR3 Gaia DR3 source_id
21- 43 E23.17 --- P [0/1] Probability of being a SMC/MW star (1)
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Note (1): A Probability value close to 1 (0) means a high probability of
being a SMC (MW) star.
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Acknowledgements:
Oscar Jimenez-Arranz, ojimenez(at)icc.ub.edu, oscar.jimenez.1996(at)gmail.com
Institut de Ciencies del Cosmos - Universitat de Barcelona (ICCUB), Spain
(End) Oscar Jimenez-Arranz [ICCUB, Spain], Patricia Vannier [CDS] 23-Mar-2023