Deep Potential: Recovering the gravitational potential and local pattern speed in the solar neighborhood with GDR3 using normalizing flows

2025/07/04 by Kalda, Taavet, Green, Gregory M. · 2 citations
#Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences

paper · doi:10.48550/arxiv.2507.03742

Abstract

The gravitational potential of the Milky Way encodes information about the distribution of all matter -- including dark matter -- throughout the Galaxy. Gaia data release 3 has revealed a complex structure that necessitates flexible models of the Galactic gravitational potential. We make use of a sample of 5.6 million upper-main-sequence stars to map the full 3D gravitational potential in a one-kiloparsec radius from the Sun using a data-driven approach called ``Deep Potential''. This method makes minimal assumptions about the dynamics of the Galaxy -- that the stars are a collisionless system that is statistically stationary in a rotating frame (with pattern speed to be determined). We model the distribution of stars in 6D phase space using a normalizing flow and the gravitational network using a neural network. We recover a local pattern speed of Ω_p = 28.2±0.1 km/s/kpc, a local total matter density of ρ=0.086±0.010 M_\odot/pc³ and local dark matter density of ρ_DM=0.007±0.011 M_\odot/pc³. The full 3D model exhibits spatial fluctuations, which may stem from the model architecture and non-stationarity in the Milky Way.

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Cited by

• ClearPotential: Revealing Local Dark Matter in Three Dimensions
• Determining the Milky Way gravitational potential without selection functions

Related

• Deep Potential: Recovering the gravitational potential from a snapshot of phase space
• Recovering the gravitational potential in a rotating frame: Deep Potential applied to a simulated barred galaxy
• ClearPotential: Revealing Local Dark Matter in Three Dimensions
• Determining the Milky Way gravitational potential without selection functions
• Mapping Dark Matter in the Milky Way using Normalizing Flows and Gaia DR3