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Deep Potential: Recovering the gravitational potential from a snapshot of phase space
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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. We first train a normalizing flow on a sample of observed phase-space positions, obtaining a smooth, differentiable approximation of the phase-space 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 is far more flexible than previous parametric methods, which fit narrow classes of analytic models to the data. This is a promising approach to uncovering the density structure of the Milky Way, using rich datasets of stellar kinematics that will soon become available.
Forward citations
Cited by 4 Pith papers
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Identification of 30,000 White Dwarf-Main Sequence binaries candidates from Gaia DR3 BP/RP(XP) low-resolution spectra
A Gaussian Process Classifier on Gaia XP spectra identifies about 30,000 white dwarf-main sequence binary candidates, with about 1,700 high-confidence systems, and GALEX UV excess validates a subset.
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Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows
Using normalizing flows and a neural network on Gaia DR3 data, the authors recover a local pattern speed of 28.2 km/s/kpc and a total matter density of 0.086 solar masses per cubic parsec within 1 kpc of the Sun.
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JFlow: Model-Independent Spherical Jeans Analysis using Equivariant Continuous Normalizing Flows
JFlow uses equivariant continuous normalizing flows and kernel-density-estimated projected likelihoods to perform model-independent spherical Jeans analysis, recovering dark matter mass densities in a mock dwarf spher...
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Denoising Milky Way stellar survey data with normalizing flow models
A normalizing flow with importance-sampling denoising partially recovers kinematic substructures (Hercules stream, phase spiral) from mock Gaia data with amplified errors.
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