arXiv Machine Learning By Florian List, Yujin Park, Nicholas L. Rodd, Eve Schoen, Florian Wolf

On the Energy Distribution of the Galactic Center Excess' Sources

Read the original on arXiv Machine Learning →

arXiv:2507. 17804v2 Announce Type: replace-cross Abstract: The Galactic Center Excess (GCE) may yet herald the discovery of annihilating dark matter.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 6

Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models

arXiv:2512. 10222v2 Announce Type: replace-cross Abstract: Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions.

By Natal\'i S. M. de Santi, Francisco Villaescusa-Navarro, Pablo Araya-Araya, Gabriella De Lucia, Fabio Fontanot, Lucia A. Perez, Manuel Arn\'es-Curto, Violeta Gonzalez-Perez, \'Angel Chandro-G\'omez, Rachel S. Somerville, Tiago Castro
arXiv Computer Vision
Sep 7

Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models

The paper presents a fully automated method for reconstructing the surface mass density of galaxy clusters using photometry and gravitational lensing data. It introduces DarkClusters-15k, a benchmark dataset of 15,000 simulated clusters with paired mass and photometry maps across multiple redshifts and simulation frameworks. By training a diffusion prior on this dataset, the authors generate posterior samples constrained by weak- and strong-lensing observables, achieving accurate, physics‑guided reconstructions with well‑calibrated uncertainties in minutes.

By Diego Royo, Brandon Zhao, Adolfo Mu\~noz, Diego Gutierrez, Katherine L. Bouman
arXiv Machine Learning
2d ago

Gestalt: a meta-foundation model for astronomy

arXiv:2609.38312v1 Announce Type: cross Abstract: The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each...

By Michael J. Smith, Shashwat Sourav