11 citations · 28 across the 10 of their papers we have counts for
10 papers
Cluster Mass Inference from Galaxy Kinematics
Bonny Y. Wang, Leander Thiele, Matthew Ho
The masses of galaxy clusters carry cosmological and astrophysical information. We develop a simulation-based inference pipeline to infer cluster masses from full projected phase-s…
The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Speed Distributions
Ethan Lilie, Jonah C. Rose, Mariangela Lisanti +14
Direct detection experiments require information about the local dark matter speed distribution to produce constraints on dark matter candidates, or infer their properties in the e…
The DREAMS Project: A New Suite of 1,024 Simulations to Contextualize the Milky Way and Assess Physics Uncertainties
Jonah C. Rose, Mariangela Lisanti, Paul Torrey +17
We introduce a new suite of 1,024 cosmological and hydrodynamical zoom-in simulations of Milky Way-mass halos, run with Cold Dark Matter, as part of the DREAMS Project. Each simula…
The Denario project: Deep knowledge AI agents for scientific discovery
Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo +33
We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the…
On the sensitivity of different galaxy properties to warm dark matter
Belén Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro +7
We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We be…
Set-based Implicit Likelihood Inference of Galaxy Cluster Mass
Bonny Y. Wang, Leander Thiele
We present a set-based machine learning framework that infers posterior distributions of galaxy cluster masses from projected galaxy dynamics. Our model combines Deep Sets and cond…
Confined tumbling state as the origin of the excess of slowly rotating asteroids
Wen-Han Zhou, Patrick Michel, Marco Delbo +4
The rotational distribution of asteroids as a function of their size is used {as a diagnostic of} their physical properties and evolution. Recent photometric surveys from the Gaia…
Cosmology from one galaxy in a void?
Bonny Y. Wang, Alice Pisani
Understanding galaxy properties may be the key to unlocking some of the most intriguing mysteries of modern cosmology. Recent work relied on machine learning to extract cosmologica…
Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine learning and Simulations
Jonah C. Rose, Paul Torrey, Francisco Villaescusa-Navarro +27
We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and…
Machine-learning cosmology from void properties
Bonny Y. Wang, Alice Pisani, Francisco Villaescusa-Navarro +1
Cosmic voids are the largest and most underdense structures in the Universe. Their properties have been shown to encode precious information about the laws and constituents of the…