170 citations · 521 across the 17 of their papers we have counts for
23 papers
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…
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Licong Xu, Milind Sarkar, Anto I. Lonappan +23
We present a multi-agent system for automation of scientific research tasks, cmbagent (https://github.com/CMBAgents/cmbagent). The system is formed by about 30 Large Language Model…
Robust Field-level Likelihood-free Inference with Galaxies
Natalí S. M. de Santi, Helen Shao, Francisco Villaescusa-Navarro +12
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our mode…
Robust field-level inference with dark matter halos
Helen Shao, Francisco Villaescusa-Navarro, Pablo Villanueva-Domingo +13
We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain…
Boosting the 21 cm forest signals by the clumpy substructures
Kenji Kadota, Pablo Villanueva-Domingo, Kiyotomo Ichiki +2
We study the contribution of subhalos to the 21 cm forest signal. The halos can host the substructures and including the effects of those small scale clumps can potentially boost t…
Learning cosmology and clustering with cosmic graphs
Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We emp…