From the 1 of 7 linked papers with an AI index.
7 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…
Inferring the Morphology of the Galactic Center Excess with Gaussian Processes
Edward D. Ramirez, Yitian Sun, Matthew R. Buckley +2
Descriptions of the Galactic Center using Fermi gamma-ray data have so far modeled the Galactic Center Excess (GCE) as a template with fixed spatial morphology or as a linear combi…