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Possibilities of identifying members from Milky Way satellite galaxies using unsupervised machine learning algorithms

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Abstract

A detailed study of stellar populations in Milky Way (MW) satellite galaxies remains an observational challenge due to their faintness and fewer spectroscopically confirmed member stars. We use unsupervised machine learning methods to identify new members for nine nearby MW satellite galaxies using Gaia data release-3 (Gaia DR3) astrometry, the Dark Energy Survey (DES) and the DECam Local Volume Exploration Survey (DELVE) photometry. Two density-based clustering algorithms, DBSCAN and HDBSCAN, have been used in the four-dimensional astrometric parameter space (\(\alpha _{2016}\), \(\delta _{2016}\), \(\mu _{\alpha } \cos \delta \), \(\mu _\delta \)) to identify member stars belonging to MW satellite galaxies. Our results indicate that we can recover more than 80% of the known spectroscopically confirmed members in most satellite galaxies and also reject 95–100% of spectroscopic non-members. We have also added many new members using this method. We compare our results with previous studies using photometric and astrometric data and discuss the suitability of density-based clustering methods for MW satellite galaxies.

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  1. https://pypi.org/project/hdbscan/.

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Acknowledgements

We thank Josh Simon and Tom Brown for sharing the relevant data, which includes satellite membership from their spectroscopic study. This work has made use of public archival data from the European Space Agency (ESA) mission Gaia DR3 (http://www.cosmos.esa.int/gaia) and the Dark Energy Survey (DES: https://www.darkenergysurvey.org/). The python packages used in this project are astropy (Astropy Collaboration et al. 2013), scikit-learn (https://scikit-learn.org) and hdbscan (https://pypi.org/project/hdbscan/). This research has used the VizieR catalog access tool, CDS, NASA’s Astrophysics Data System Bibliographic Services and TOPCAT software18 (Taylor 2011).

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Authors and Affiliations

  1. Indian Institute of Astrophysics, II Block, Koramangala, Bangalore, 560034, India

    Devika K. Divakar, Pallavi Saraf & Thirupathi Sivarani

  2. Department of Physics, Bangalore University, Bangalore, 560056, India

    Devika K. Divakar & Vijayakumar H. Doddamani

  3. Pondicherry University, R.V. Nagar, Kalapet, 605014, India

    Pallavi Saraf

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  1. Devika K. Divakar
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Appendices

Appendix A: Selected hyperparameters

See Table 3.

Table 3 Optimized hyperparameters for DBSCAN and HDBSCAN.
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Fig. 10
figure 10

Same as Figure 4, but for CarIII.

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Fig. 11
figure 11

Same as Figure 4, but for CarII.

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Appendix B: Resultant plots

Fig. 12
figure 12

Same as Figure 4, but for BoöII.

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Fig. 13
figure 13

Same as Figure 4, but for BoöIII.

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Fig. 14
figure 14

Same as Figure 4, but for TucIV.

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Fig. 15
figure 15

Same as Figure 4, but for BoöI.

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Fig. 16
figure 16

Comparison of the median proper motion of candidate members from DBSCAN (orange square) and HDBSCAN (cyan square) with other literature values (filled circles with different colors). The error bar shows the standard deviation derived from the proper motion of candidate members. Reference: (1) Kallivayalil et al. (2018; 2) Carlin & Sand (2018; 3) Massari & Helmi (2018; 4) Gaia Collaboration et al. (2018b; 5) Fritz et al. (2018; 6) Simon (2018; 7) Pace & Li (2019); (8) Simon et al. (2020; 9) McConnachie & Venn (2020a; 10) McConnachie & Venn (2020b; 11) Vitral (2021; 12) Li et al. (2021; 13) Bruce et al. (2023; 14) Martínez-García et al. (2021; 15) Longeard et al. (2022; 16) Battaglia et al. (2022) and (17) Pace et al. (2022).

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The resultant plots for UFDs using DBSCAN and HDBSCAN algorithms are shown in Figures 9–15. Figure 16 shows the calculated proper motion from the identified candidates of each UFD using DBSCAN and HDBSCAN in comparison with the literature values.

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Divakar, D.K., Saraf, P., Sivarani, T. et al. Possibilities of identifying members from Milky Way satellite galaxies using unsupervised machine learning algorithms. J Astrophys Astron 45, 5 (2024). https://doi.org/10.1007/s12036-023-09990-4

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