J/MNRAS/489/2189 Nearby young moving groups with machine learning (Lee+, 2019)
Evaluation of nearby young moving groups based on unsupervised machine learning.
Lee J., Song I.
<Mon. Not. R. Astron. Soc., 489, 2189-2194 (2019)>
=2019MNRAS.489.2189L 2019MNRAS.489.2189L (SIMBAD/NED BibCode)
ADC_Keywords: Stars, nearby ; Clusters, open ; Spectral types ; Models ; VLBI
Keywords: methods: data analysis - open clusters and associations: general -
solar neighbourhood
Abstract:
Nearby young stellar moving groups have been identified by many
research groups with different methods and criteria, giving rise to
caution regarding the reality of some groups. We aim to utilize moving
groups in an unbiased way to create a list of unambiguously
recognizable moving groups and their members. For the analysis, two
unsupervised machine-learning algorithms (K-means and Agglomerative
Clustering) are applied to previously known bona fide members of nine
moving groups from our previous study. As a result of this study, we
recovered six previously known groups (AB Doradus, Argus, β Pic,
Carina, TWA and Volans-Carina). Three other known groups are
recognized as well; however, they are combined into two new separate
groups (ThOr+Columba and TucHor+Columba).
Description:
As input data, we use bona fide members of nine NYMGs from Lee & Song
(2019MNRAS.486.2412L 2019MNRAS.486.2412L; Paper I), which enable us to evaluate NYMGs by
comparing clustering results with previously known NYMGs. The nine
groups considered are TWA, BPMG, ThOr, TucHor, Carina, Columba, Argus,
ABDor and VCA. While the spatio-kinematic information for the stars,
XYZ and UVW, is straightforward to calculate, their age is difficult
to obtain and generally has a large uncertainty. In the age range of
NYMGs (∼8 to ∼100Myr), age is evaluated using Li equivalent width,
position on colour-magnitude diagrams, near-UV excess or X-ray
brightness (e.g. Zuckerman & Song 2004ARA&A..42..685Z 2004ARA&A..42..685Z, Cat.
J/other/ARA+A/42.685; Soderblom 2010ARA&A..48..581S 2010ARA&A..48..581S; Rodriguez et al.
2011ApJ...727...62R 2011ApJ...727...62R; Malo et al. 2013ApJ...762...88M 2013ApJ...762...88M, Cat.
J/ApJ/762/88).
Clustering algorithms find groups in data in such a way that members
in the same group are more similar to each other than to those in
other groups. There are various clustering algorithms, with different
strategies and criteria for finding groups. In this study, we use two
algorithms in the SKLEARN package of python (Pedregosa et al.
2011JMLR...12.2825P 2011JMLR...12.2825P). Each algorithm has its own specific parameters.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 94 599 Sample bona fide members of the newly defined
groups
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Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 3 I3 --- ID [0/598] Internal object identifier
5- 30 A26 --- Name Star name
32- 43 A12 --- SpType Spectral type
45- 64 F20.16 deg RAdeg Right ascension (J2000)
66- 85 F20.16 deg DEdeg Declination (J2000)
87- 94 A8 --- Group Nearby young stellar moving group (NYMG) (1)
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Note (1): Group as follows:
ABDor = AB Doradus moving group (119/599)
Argus = Argus association (42/559)
BPMG = β Pic moving group (96/599)
Carina = Carina association (34/599)
THC = Group containing almost the entire Tucana-Horologium association
(TucHor) and half the Columba members (210/599)
TWA = TW Hya association (31/599)
ThOr-Col = Group containing the entire 32 Ori association (ThOr) and a
subset of Columba members (49/559)
VCA = Volans-Carina association (18/599)
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History:
From electronic version of the journal
References:
Lee & Song, Paper I 2019MNRAS.486.2412L 2019MNRAS.486.2412L
(End) Ana Fiallos [CDS] 10-Jan-2023