From the 1 of 5 linked papers with an AI index.
5 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 authors identify a near‑universal dark‑matter phase‑space distribution in Milky Way‑mass galaxy simulations, show it can be inferred from metal‑poor stellar data, and construct…
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…
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…
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…