Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space
One of the major goals of the field of Milky Way dynamics is to recover the gravitational potential field. Mapping the potential would allow us to determine the spatial distribution of matter—both baryonic and dark—throughout the galaxy. We present a novel method for determining the gravitational fi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4357/aca3a7 |
| _version_ | 1797697248531841024 |
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| author | Gregory M. Green Yuan-Sen Ting Harshil Kamdar |
| author_facet | Gregory M. Green Yuan-Sen Ting Harshil Kamdar |
| author_sort | Gregory M. Green |
| collection | DOAJ |
| description | One of the major goals of the field of Milky Way dynamics is to recover the gravitational potential field. Mapping the potential would allow us to determine the spatial distribution of matter—both baryonic and dark—throughout the galaxy. We present a novel method for determining the gravitational field from a snapshot of the phase-space positions of stars, based only on minimal physical assumptions, which makes use of recently developed tools from the field of deep learning. We first train a normalizing flow on a sample of observed six-dimensional phase-space coordinates of stars, obtaining a smooth, differentiable approximation of the distribution function. Using the collisionless Boltzmann equation, we then find the gravitational potential—represented by a feed-forward neural network—that renders this distribution function stationary. This method, which we term “Deep Potential,” is more flexible than previous parametric methods, which fit restricted classes of analytic models of the distribution function and potential to the data. We demonstrate Deep Potential on mock data sets and demonstrate its robustness under various nonideal conditions. Deep Potential is a promising approach to mapping the density of the Milky Way and other stellar systems, using rich data sets of stellar positions and kinematics now being provided by Gaia and ground-based spectroscopic surveys. |
| first_indexed | 2024-03-12T03:37:37Z |
| format | Article |
| id | doaj.art-48fb435d27544a06b01c283cdfc673c1 |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-12T03:37:37Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-48fb435d27544a06b01c283cdfc673c12023-09-03T13:07:57ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-0194212610.3847/1538-4357/aca3a7Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase SpaceGregory M. Green0https://orcid.org/0000-0001-5417-2260Yuan-Sen Ting1https://orcid.org/0000-0001-5082-9536Harshil Kamdar2https://orcid.org/0000-0001-5625-5342Max Planck Institute for Astronomy Königstuhl 17, D-69117 Heidelberg, Germany ; gregorymgreen@gmail.comResearch School of Astronomy & Astrophysics, Australian National University Cotter Rd. , Weston, ACT 2611, Australia; Research School of Computer Science, Australian National University Acton , ACT 2601, AustraliaDepartment of Astronomy, Harvard University , 60 Garden St., Cambridge, MA 02138, USAOne of the major goals of the field of Milky Way dynamics is to recover the gravitational potential field. Mapping the potential would allow us to determine the spatial distribution of matter—both baryonic and dark—throughout the galaxy. We present a novel method for determining the gravitational field from a snapshot of the phase-space positions of stars, based only on minimal physical assumptions, which makes use of recently developed tools from the field of deep learning. We first train a normalizing flow on a sample of observed six-dimensional phase-space coordinates of stars, obtaining a smooth, differentiable approximation of the distribution function. Using the collisionless Boltzmann equation, we then find the gravitational potential—represented by a feed-forward neural network—that renders this distribution function stationary. This method, which we term “Deep Potential,” is more flexible than previous parametric methods, which fit restricted classes of analytic models of the distribution function and potential to the data. We demonstrate Deep Potential on mock data sets and demonstrate its robustness under various nonideal conditions. Deep Potential is a promising approach to mapping the density of the Milky Way and other stellar systems, using rich data sets of stellar positions and kinematics now being provided by Gaia and ground-based spectroscopic surveys.https://doi.org/10.3847/1538-4357/aca3a7Milky Way dynamicsStellar dynamicsNeural networksGravitational fieldsAstrostatistics |
| spellingShingle | Gregory M. Green Yuan-Sen Ting Harshil Kamdar Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space The Astrophysical Journal Milky Way dynamics Stellar dynamics Neural networks Gravitational fields Astrostatistics |
| title | Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space |
| title_full | Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space |
| title_fullStr | Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space |
| title_full_unstemmed | Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space |
| title_short | Deep Potential: Recovering the Gravitational Potential from a Snapshot of Phase Space |
| title_sort | deep potential recovering the gravitational potential from a snapshot of phase space |
| topic | Milky Way dynamics Stellar dynamics Neural networks Gravitational fields Astrostatistics |
| url | https://doi.org/10.3847/1538-4357/aca3a7 |
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