5 papers
Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks
Charlotte Myers, Nathaniel Starkman, Lina Necib
The gravitational potential of a galaxy encodes its mass distribution, formation history, and dark matter halo structure. Accurate potential models are therefore critical for inter…
Potamides: Mapping Dark Matter Halo Shapes from Stellar Stream Tracks in the Local Universe
Sirui Wu, Nathaniel Starkman, Sarah Pearson +3
Stellar streams trace the gravitational potential of their host galaxies and offer a direct probe of dark matter halo geometry. Cosmological simulations predict that halo shapes de…
Galactic Amnesia: The Information Washout of the Milky Way Merger History
Lina Necib, Dylan Folsom, Elliot Y. Davies +2
The merger history of a galaxy leaves imprints on its present-day stellar chemodynamics, yet dynamical processes progressively erase this record. We ask: how far back in time, and…
Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentials
Charlotte Myers, Nathaniel Starkman, Lina Necib
We introduce a physics-informed neural framework for modeling static and time-dependent galactic gravitational potentials. The method combines data-driven learning with embedded ph…
unxt: A Python package for unit-aware computing with JAX
Nathaniel Starkman, Adrian Price-Whelan, Jake Nibauer
unxt is a Python package for unit-aware computing with JAX. unxt is built on top of quax, which provides a framework for building array-like objects that can be used with JAX. unxt…