activity
20152025
most citedMachine Learning and Cosmological Simulations II: Hydrodynamical Simulations

60 citations · 263 across the 10 of their papers we have counts for

collaborators

11 papers

astro-ph.SR2025

Deriving Stellar Properties, Distances, and Reddenings using Photometry and Astrometry with BRUTUS

Joshua S. Speagle, Catherine Zucker, Angus Beane +18

We present brutus, an open source Python package for quickly deriving stellar properties, distances, and reddenings to stars based on grids of stellar models constrained by photome…

astro-ph.GA2025★ 5 cited

Mapping the Milky Way in 5-D with 170 Million Stars

Joshua S. Speagle, Catherine Zucker, Ana Bonaca +18

We present "augustus", a catalog of distance, extinction, and stellar parameter estimates to 170 million stars from and with dr…

astro-ph.GA2022★ 18 cited

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

Gregory M. Green, Yuan-Sen Ting, Harshil Kamdar

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 distributio…

astro-ph.GA2021★ 7 cited

Stellar Streams in the Galactic Disk: Predicted Lifetimes and Their Utility in Measuring the Galactic Potential

Harshil Kamdar, Charlie Conroy, Yuan-Sen Ting

In this paper, we present a holistic view of the detection, characterization, and origin of stellar streams in the disk of a simulated Milky Way-like galaxy. The star-by-star simul…

astro-ph.SR2021★ 38 cited

Distant Relatives: The Chemical Homogeneity of Comoving Pairs Identified in Gaia

Tyler Nelson, Yuan-Sen Ting, Keith Hawkins +3

Comoving pairs, even at the separations of AU, are a predicted reservoir of conatal stars. We present detailed chemical abundances of 62 stars in 31 comoving p…

astro-ph.GA2020★ 9 cited

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

Gregory M. Green, Yuan-Sen Ting

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 distributio…

astro-ph.GA2020★ 11 cited

Spatial and Kinematic Clustering of Stars in the Galactic Disk

Harshil Kamdar, Charlie Conroy, Yuan-Sen Ting +1

The Galactic disk is expected to be spatially, kinematically, and chemically clustered on many scales due to both star formation and non-axisymmetries in the Galactic potential. In…

astro-ph.GA2019★ 38 cited

Stars that Move Together Were Born Together

Harshil Kamdar, Charlie Conroy, Yuan-Sen Ting +3

It is challenging to reliably identify stars that were born together outside of actively star-forming regions and bound stellar systems. However, co-natal stars should be present t…

astro-ph.GA2019★ 28 cited

A Dynamical Model for Clustered Star Formation in the Galactic Disk

Harshil Kamdar, Charlie Conroy, Yuan-Sen Ting +3

The clustered nature of star formation should produce a high degree of structure in the combined phase and chemical space in the Galactic disk. To date, observed structure of this…

astro-ph.GA2015★ 49 cited

Machine Learning and Cosmological Simulations I: Semi-Analytical Models

Harshil M. Kamdar, Matthew J. Turk, Robert J. Brunner

We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenti…

astro-ph.GA2015★ 60 cited

Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations

Harshil M. Kamdar, Matthew J. Turk, Robert J. Brunner

We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In th…