Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks
The phase space of stellar streams is proposed to detect dark substructure in the Milky Way through the perturbations created by passing subhalos—and thus is a powerful test of the cold dark matter paradigm and its alternatives. Using graph convolutional neural network (GCNN) data compression and si...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/add698 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850106791673724928 |
|---|---|
| author | Peter Xiangyuan Ma Keir K. Rogers Ting S. Li Renée Hložek Jeremy J. Webb Ruth Huang Julian Meunier |
| author_facet | Peter Xiangyuan Ma Keir K. Rogers Ting S. Li Renée Hložek Jeremy J. Webb Ruth Huang Julian Meunier |
| author_sort | Peter Xiangyuan Ma |
| collection | DOAJ |
| description | The phase space of stellar streams is proposed to detect dark substructure in the Milky Way through the perturbations created by passing subhalos—and thus is a powerful test of the cold dark matter paradigm and its alternatives. Using graph convolutional neural network (GCNN) data compression and simulation-based inference (SBI) on a simulated GD-1-like stream, we improve the constraint on the mass of a [10 ^8 , 10 ^7 , 10 ^6 ] M _⊙ perturbing subhalo by factors of [11, 7, 3] with respect to the current state-of-the-art density power spectrum analysis. We find that the GCNN produces posteriors that are more accurate (better calibrated) than the power spectrum. We simulate the positions and velocities of stars in a GD-1-like stream and perturb the stream with subhalos of varying mass and velocity. Leveraging the feature encoding of the GCNN to compress the input phase space data, we then use SBI to estimate the joint posterior of the subhalo mass and velocity. We investigate how our results scale with the size of the GCNN, the coordinate system of the input, and the effect of incomplete observations. Our results suggest that a survey with 10× fewer stars (300 stars) with complete 6D phase space data performs about as well as a deeper survey (3000 stars) with only 3D data (photometry, spectroscopy). The stronger constraining power and more accurate posterior estimation motivate further development of GCNNs in combining future photometric, spectroscopic, and astrometric stream observations. |
| format | Article |
| id | doaj-art-44dae0dbbc3e448995992b2e1974579c |
| institution | OA Journals |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-44dae0dbbc3e448995992b2e1974579c2025-08-20T02:38:45ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-0198719610.3847/1538-4357/add698Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural NetworksPeter Xiangyuan Ma0https://orcid.org/0000-0001-8975-3719Keir K. Rogers1Ting S. Li2https://orcid.org/0000-0002-9110-6163Renée Hložek3https://orcid.org/0000-0002-0965-7864Jeremy J. Webb4https://orcid.org/0000-0003-3613-0854Ruth Huang5Julian Meunier6Department of Astronomy , UC Berkeley, 501 Campbell Hall, Berkeley, CA, 94720, USA ; peter_ma@berkeley.edu; Department of Mathematics, University of Toronto , 40 St. George Street, Toronto, ON, M5S 2E4, CanadaDepartment of Physics, Imperial College London , Blackett Laboratory, Prince Consort Road, London, SW7 2AZ, UK; Dunlap Institute for Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON, M5S 3H4, CanadaDavid A. Dunlap Department of Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON, M5S 3H4, CanadaDunlap Institute for Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON, M5S 3H4, Canada; David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON, M5S 3H4, CanadaDavid A. Dunlap Department of Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON, M5S 3H4, Canada; Department of Science, Technology and Society, Division of Natural Science, York University , 218 Bethune College, Toronto, ON, M3J 1P3, CanadaDavid A. Dunlap Department of Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON, M5S 3H4, CanadaDavid A. Dunlap Department of Astronomy and Astrophysics, University of Toronto , 50 St. George Street, Toronto, ON, M5S 3H4, CanadaThe phase space of stellar streams is proposed to detect dark substructure in the Milky Way through the perturbations created by passing subhalos—and thus is a powerful test of the cold dark matter paradigm and its alternatives. Using graph convolutional neural network (GCNN) data compression and simulation-based inference (SBI) on a simulated GD-1-like stream, we improve the constraint on the mass of a [10 ^8 , 10 ^7 , 10 ^6 ] M _⊙ perturbing subhalo by factors of [11, 7, 3] with respect to the current state-of-the-art density power spectrum analysis. We find that the GCNN produces posteriors that are more accurate (better calibrated) than the power spectrum. We simulate the positions and velocities of stars in a GD-1-like stream and perturb the stream with subhalos of varying mass and velocity. Leveraging the feature encoding of the GCNN to compress the input phase space data, we then use SBI to estimate the joint posterior of the subhalo mass and velocity. We investigate how our results scale with the size of the GCNN, the coordinate system of the input, and the effect of incomplete observations. Our results suggest that a survey with 10× fewer stars (300 stars) with complete 6D phase space data performs about as well as a deeper survey (3000 stars) with only 3D data (photometry, spectroscopy). The stronger constraining power and more accurate posterior estimation motivate further development of GCNNs in combining future photometric, spectroscopic, and astrometric stream observations.https://doi.org/10.3847/1538-4357/add698Galaxy dynamicsGalaxy dark matter halosNeural networksAstronomical simulations |
| spellingShingle | Peter Xiangyuan Ma Keir K. Rogers Ting S. Li Renée Hložek Jeremy J. Webb Ruth Huang Julian Meunier Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks The Astrophysical Journal Galaxy dynamics Galaxy dark matter halos Neural networks Astronomical simulations |
| title | Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks |
| title_full | Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks |
| title_fullStr | Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks |
| title_full_unstemmed | Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks |
| title_short | Toward Characterizing Dark Matter Subhalo Perturbations in Stellar Streams with Graph Neural Networks |
| title_sort | toward characterizing dark matter subhalo perturbations in stellar streams with graph neural networks |
| topic | Galaxy dynamics Galaxy dark matter halos Neural networks Astronomical simulations |
| url | https://doi.org/10.3847/1538-4357/add698 |
| work_keys_str_mv | AT peterxiangyuanma towardcharacterizingdarkmattersubhaloperturbationsinstellarstreamswithgraphneuralnetworks AT keirkrogers towardcharacterizingdarkmattersubhaloperturbationsinstellarstreamswithgraphneuralnetworks AT tingsli towardcharacterizingdarkmattersubhaloperturbationsinstellarstreamswithgraphneuralnetworks AT reneehlozek towardcharacterizingdarkmattersubhaloperturbationsinstellarstreamswithgraphneuralnetworks AT jeremyjwebb towardcharacterizingdarkmattersubhaloperturbationsinstellarstreamswithgraphneuralnetworks AT ruthhuang towardcharacterizingdarkmattersubhaloperturbationsinstellarstreamswithgraphneuralnetworks AT julianmeunier towardcharacterizingdarkmattersubhaloperturbationsinstellarstreamswithgraphneuralnetworks |