8 citations · 15 across the 6 of their papers we have counts for
6 papers
A Universal Distribution of Dark Matter in Milky Way-like galaxies and How to Infer It
Sam Cheng-Tse Huang, Matthew R. Buckley, Justin I. Read +1
The phase-space density of dark matter within the Milky Way is a key quantity that encodes information about the nature of the dark sector. The local phase-space density is also re…
ClearPotential: Revealing Local Dark Matter in Three Dimensions
Eric Putney, David Shih, Sung Hak Lim +1
We present ClearPotential, a data-driven, three-dimensional measurement of the gravitational potential of the local Milky Way using unsupervised machine learning, without the symme…
Via Machinae 3.0: A search for stellar streams in Gaia with the CATHODE algorithm
Anna Hallin, David Shih, Claudius Krause +1
We apply the model-agnostic anomaly detection method Cathode - originally developed for particle physics - to search for stellar streams in Gaia data. We combine Cathode with Via M…
Sweeping the Dust Away -- Correcting the Phase Space Density of the Milky Way with Unsupervised Machine Learning
Eric Putney, David Shih, Sung Hak Lim +1
The Boltzmann equation relates the equilibrium phase space distribution of stars in the Milky Way to the Galaxy's gravitational potential. However, observations of stellar populati…
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka +66
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of i…
GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Mock Stellar Catalogs
Sung Hak Lim, Kailash A. Raman, Matthew R. Buckley +1
Cosmological N-body simulations of galaxies operate at the level of "star particles" with a mass resolution on the scale of thousands of solar masses. Turning these simulations int…