Deep Potential: Recovering the gravitational potential from a snapshot of phase space

Green, Gregory; Ting, Yuan-Sen; Kamdar, Harshil

doi:10.48550/arxiv.2205.02244

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…However, we emphasize that basis expansions can equally be applied to constrain a potential from our estimates of the acceleration field. Following the approach of Green & Ting (2020) and Green et al (2022), we represent the potential Φ with a flexible fully connected neural network Φ β (x), where β are the parameters of the neural network. Using estimates of the acceleration field localized to several streams, Φ β can be trained through its spatial gradient to reproduce the local acceleration field of stellar streams while interpolating over the unsampled regions.…”

Section: Constraining a Flexible Potentialmentioning

confidence: 99%

Charting Galactic Accelerations with Stellar Streams and Machine Learning

Nibauer

Belokurov

Cranmer

et al. 2022

ApJ

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We present a data-driven method for reconstructing the galactic acceleration field from phase-space (position and velocity) measurements of stellar streams. Our approach is based on a flexible and differentiable fit to the stream in phase-space, enabling a direct estimate of the acceleration vector along the stream. Reconstruction of the local acceleration field can be applied independently to each of several streams, allowing us to sample the acceleration field due to the underlying galactic potential across a range of scales. Our approach is methodologically different from previous works, as a model for the gravitational potential does not need to be adopted beforehand. Instead, our flexible neural-network-based model treats the stream as a collection of orbits with a locally similar mixture of energies, rather than assuming that the stream delineates a single stellar orbit. Accordingly, our approach allows for distinct regions of the stream to have different mean energies, as is the case for real stellar streams. Once the acceleration vector is sampled along the stream, standard analytic models for the galactic potential can then be rapidly constrained. We find our method recovers the correct parameters for a ground-truth triaxial logarithmic halo potential when applied to simulated stellar streams. Alternatively, we demonstrate that a flexible potential can be constrained with a neural network, and standard multipole expansions can also be constrained. Our approach is applicable to simple and complicated gravitational potentials alike and enables potential reconstruction from a fully data-driven standpoint using measurements of slowly phase-mixing tidal debris.

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Section: Constraining a Flexible Potentialmentioning

confidence: 99%

Charting Galactic Accelerations with Stellar Streams and Machine Learning

Nibauer

Belokurov

Cranmer

et al. 2022

ApJ

View full text Add to dashboard Cite

show abstract

“…However, we emphasize that basis expansions can equally be applied to constrain a potential from our estimates of the acceleration field. Following the approach of Green & Ting (2020); Green et al (2022), we represent the potential Φ with a flexible fully-connected neural network Φ β (x), where β are the parameters of the neural network. Using estimates of the acceleration field localized to several streams, Φ β can be trained through its spatial gradient to reproduce the local acceleration field of stellar streams while interpolating over the unsampled regions.…”

Section: Constraining a Flexible Potentialmentioning

confidence: 99%

Charting Galactic Accelerations with Stellar Streams and Machine Learning

Nibauer¹,

Belokurov²,

Cranmer³

et al. 2022

Preprint

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We present a data-driven method for reconstructing the galactic acceleration field from phase-space measurements of stellar streams. Our approach is based on a flexible and differentiable fit to the stream in phase-space, enabling a direct estimate of the acceleration vector along the stream. Reconstruction of the local acceleration field can be applied independently to each of several streams, allowing us to sample the acceleration field due to the underlying galactic potential across a range of scales. Our approach is methodologically different from previous works, since a model for the gravitational potential does not need to be adopted beforehand. Instead, our flexible neural-network-based model treats the stream as a collection of orbits with a locally similar mixture of energies, rather than assuming that the stream delineates a single stellar orbit. Accordingly, our approach allows for distinct regions of the stream to have different mean energies, as is the case for real stellar streams. Once the acceleration vector is sampled along the stream, standard analytic models for the galactic potential can then be rapidly constrained. We find our method recovers the correct parameters for a groundtruth triaxial logarithmic halo potential when applied to simulated stellar streams. Alternatively, we demonstrate that a flexible potential can be constrained with a neural network, though standard multipole expansions can also be constrained. Our approach is applicable to simple and complicated gravitational potentials alike, and enables potential reconstruction from a fully data-driven standpoint using measurements of slowly phase-mixing tidal debris.

show abstract

Deep Potential: Recovering the gravitational potential from a snapshot of phase space

Cited by 2 publications

References 38 publications

Charting Galactic Accelerations with Stellar Streams and Machine Learning

Charting Galactic Accelerations with Stellar Streams and Machine Learning

Charting Galactic Accelerations with Stellar Streams and Machine Learning

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