Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density Networks
Stellar streams are sensitive probes of the Milky Way’s gravitational potential. The mean track of a stream constrains global properties of the potential, while its fine-grained surface density constrains galactic substructure. A precise characterization of streams from potentially noisy data marks...
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| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad94f2 |
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| author | Nathaniel Starkman Jacob Nibauer Jo Bovy Jeremy J. Webb Kiyan Tavangar Adrian Price-Whelan Ana Bonaca |
| author_facet | Nathaniel Starkman Jacob Nibauer Jo Bovy Jeremy J. Webb Kiyan Tavangar Adrian Price-Whelan Ana Bonaca |
| author_sort | Nathaniel Starkman |
| collection | DOAJ |
| description | Stellar streams are sensitive probes of the Milky Way’s gravitational potential. The mean track of a stream constrains global properties of the potential, while its fine-grained surface density constrains galactic substructure. A precise characterization of streams from potentially noisy data marks a crucial step in inferring galactic structure, including the dark matter, across orders of magnitude in mass scales. Here we present a new method for constructing a smooth probability density model of stellar streams using all of the available astrometric and photometric data. To characterize a stream’s morphology and kinematics, we utilize mixture density networks to represent its on-sky track, width, stellar number density, and kinematic distribution. We model the photometry for each stream as a single-stellar population, with a distance track that is simultaneously estimated from the stream’s inferred distance modulus (using photometry) and parallax distribution (using astrometry). We use normalizing flows to characterize the distribution of background stars. We apply the method to the stream GD-1, and the tidal tails of Palomar 5. For both streams we obtain a catalog of stellar membership probabilities that are made publicly available. Importantly, our model is capable of handling data with incomplete phase-space observations, making our method applicable to the growing census of Milky Way stellar streams. |
| format | Article |
| id | doaj-art-ce8fbc4cca8d46dcaf84f0e2f955354f |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-ce8fbc4cca8d46dcaf84f0e2f955354f2025-08-20T03:49:21ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01980225310.3847/1538-4357/ad94f2Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density NetworksNathaniel Starkman0https://orcid.org/0000-0003-3954-3291Jacob Nibauer1https://orcid.org/0000-0001-8042-5794Jo Bovy2https://orcid.org/0000-0001-6855-442XJeremy J. Webb3https://orcid.org/0000-0003-3613-0854Kiyan Tavangar4https://orcid.org/0000-0001-6584-6144Adrian Price-Whelan5https://orcid.org/0000-0003-0872-7098Ana Bonaca6https://orcid.org/0000-0002-7846-9787David A Dunlap Department of Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto ON M5S 3H4, Canada ; n.starkman@mail.utoronto.caDepartment of Astrophysical Sciences, Princeton University , 4 Ivy Lane, Princeton, NJ 08544, USA; The Observatories of the Carnegie Institution for Science , 813 Santa Barbara Street, Pasadena, CA 91101, USADavid A Dunlap Department of Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto ON M5S 3H4, Canada ; n.starkman@mail.utoronto.caDepartment of Science, Technology and Society, York University , 4700 Keele Street, Toronto ON M3J 1P3, CanadaDepartment of Astronomy, Columbia University , 538 West 120th Street, New York, NY 10027, USACenter for Computational Astrophysics , Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USAThe Observatories of the Carnegie Institution for Science , 813 Santa Barbara Street, Pasadena, CA 91101, USAStellar streams are sensitive probes of the Milky Way’s gravitational potential. The mean track of a stream constrains global properties of the potential, while its fine-grained surface density constrains galactic substructure. A precise characterization of streams from potentially noisy data marks a crucial step in inferring galactic structure, including the dark matter, across orders of magnitude in mass scales. Here we present a new method for constructing a smooth probability density model of stellar streams using all of the available astrometric and photometric data. To characterize a stream’s morphology and kinematics, we utilize mixture density networks to represent its on-sky track, width, stellar number density, and kinematic distribution. We model the photometry for each stream as a single-stellar population, with a distance track that is simultaneously estimated from the stream’s inferred distance modulus (using photometry) and parallax distribution (using astrometry). We use normalizing flows to characterize the distribution of background stars. We apply the method to the stream GD-1, and the tidal tails of Palomar 5. For both streams we obtain a catalog of stellar membership probabilities that are made publicly available. Importantly, our model is capable of handling data with incomplete phase-space observations, making our method applicable to the growing census of Milky Way stellar streams.https://doi.org/10.3847/1538-4357/ad94f2Stellar streamsGlobular star clustersAstrodynamicsOrbit determinationGalaxy structureMilky Way dark matter halo |
| spellingShingle | Nathaniel Starkman Jacob Nibauer Jo Bovy Jeremy J. Webb Kiyan Tavangar Adrian Price-Whelan Ana Bonaca Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density Networks The Astrophysical Journal Stellar streams Globular star clusters Astrodynamics Orbit determination Galaxy structure Milky Way dark matter halo |
| title | Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density Networks |
| title_full | Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density Networks |
| title_fullStr | Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density Networks |
| title_full_unstemmed | Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density Networks |
| title_short | Stream Members Only: Data-driven Characterization of Stellar Streams with Mixture Density Networks |
| title_sort | stream members only data driven characterization of stellar streams with mixture density networks |
| topic | Stellar streams Globular star clusters Astrodynamics Orbit determination Galaxy structure Milky Way dark matter halo |
| url | https://doi.org/10.3847/1538-4357/ad94f2 |
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