530 citations · 7.2k across the 198 of their papers we have counts for
209 papers
Field-level inference under pressure: the information content of the thermal Sunyaev-Zel'dovich field
Carmen Embil Villagra, Adrian E. Bayer, William R. Coulton +6
The thermal Sunyaev--Zel'dovich (tSZ) field is highly non-Gaussian and contains cosmological information that cannot be fully captured by its angular power spectrum. In this work,…
AI-assisted super-resolution cosmological simulations V: Cosmology-aware super-resolution
Xiaowen Zhang, Patrick Lachance, Rupert A. C. Croft +2
We present a cosmology-aware generative model for super-resolution (SR) of cosmological -body simulations in particle phase space. The model jointly generates displacements and…
EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights
Jiayi Geng, Zhengxuan Wu, Kevin S. Chen +12
When Isaac Newton discovered the law of gravitation, he did so through an iterative process of analyzing observed data such as planetary patterns, finding the underlying mechanisms…
FOTO: Figure-Level Semantic Search for Astronomy
Hurum Maksora Tohfa, Francisco Villaescusa-Navarro
Astronomy papers carry much of their content in figures, but literature search indexes text, so there is no way to find a plot by describing what it shows. We present FOTO, a figur…
The CAMELS-CROCODILE Simulation Suite: A New Cosmology--Astrophysics Playground for Machine Learning
Kentaro Nagamine, Yuri Oku, Atsushi J. Nishizawa +4
We present CAMELS-CROCODILE, a suite of cosmological hydrodynamic simulations that extends the CAMELS framework with the \gadget{} smoothed particle hydrodynamics code and the Osak…
Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs
James O. Baldwin, Shy Genel, Francisco Villaescusa-Navarro
We perform field-level likelihood-free inference of the matter density parameter from simulated galaxy catalogs using machine learning models with differing inductive biases.…
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions
Raul Jimenez, Boris Bolliet, Francisco Villaescusa-Navarro +7
Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code…
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
Shy Genel, Yongseok Jo, Boon Kiat Oh +10
We present a new set of 1,192 cosmological simulations as part of the CAMELS project, in which a space of 35 cosmological, astrophysical, and numerical parameters is explored aroun…
Reconciling the Fundamental Plane of Early-Type Galaxies with hydrodynamical simulations: The case of IllustrisTNG100-1
Pedro de Araujo Ferreira, Nicola R. Napolitano, Crescenzo Tortora +2
The Fundamental Plane (FP) of Early-Type Galaxies (ETGs) encapsulates a tight correlation among their structural and dynamical properties and provides an important benchmark for ga…
Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters
Christopher C. Lovell, Max E. Lee, William J. Roper +4
We present a new method for emulating the halo mass function (HMF) and other distribution functions in large effective volumes, down to low halo masses, whilst simultaneously modif…
Field-Level Inference of Primordial Non-Gaussianity with the Quijote Simulation Suite
Adam Andrews, Jens Jasche, Guilhem Lavaux +10
Local primordial non-Gaussianity, parameterised as , will be stringently constrained using state-of-the-art methods applied to next-generation galaxy redshi…
Cosmology with one galaxy: An analytic formula relating with galaxy properties
Kito Liao, Francisco Villaescusa-Navarro, Romain Teyssier +1
Standard cosmological analyses typically treat galaxy formation and cosmological parameter inference as decoupled problems, relying on population-level statistics such as clusterin…
CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations IV. Testing warm dark matter cosmologies with galaxy scaling relations: A joint simulation-observation study using DREAMS simulations
M. Silvestrini, C. Tortora, V. Busillo +9
Small-scale discrepancies in the standard Lambda cold dark matter paradigm have motivated the exploration of alternative dark matter (DM) models, such as warm dark matter (WDM). We…
CAMELS Environments: The Impact of Local Neighbours on Galaxy Evolution across the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE Simulations
Xavier Sims, Daniel Anglés-Alcázar, Boon-Kiat Oh +6
Internal feedback from massive stars and active galactic nuclei (AGN) play a key role in galaxy evolution, but external environmental effects can also strongly influence galaxies.…
Cosmological back-reaction of baryons on dark matter in the CAMELS simulations
Matthew Gebhardt, Daniel Anglés-Alcázar, Shy Genel +13
Baryonic processes such as radiative cooling and feedback from massive stars and active galactic nuclei (AGN) directly redistribute baryons in the Universe but also indirectly redi…
Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Pablo Araya-Araya +8
Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They ha…
The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Density Profiles
Alex M. Garcia, Jonah C. Rose, Paul Torrey +23
In this work, we utilize a new suite of Milky Way-mass halos from the DREAMS Project, simulated with Cold Dark Matter (CDM), to quantify the influence of baryon feedback and intrin…
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…
Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks
Ilem Leisher, Paul Torrey, Alex M. Garcia +11
Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. Cosmological simulations can represent this series of mergers as a graph-like ``tree''…
Learning Cosmology from Nearest Neighbour Statistics
Atrideb Chatterjee, Arka Banerjee, Francisco Villaescusa-Navarro +1
Extracting cosmological parameters from galaxy/halo catalogues with sub-percent level accuracy is an important aspect of modern cosmology, especially in view of ongoing and upcomin…
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…
MG-NECOLA: Fast Neural Emulators for Modified Gravity Cosmologies
J. Bayron Orjuela-Quintana, Mauricio Reyes, Elena Giusarma +3
Observations of the large-scale structure (LSS) provide a powerful test of gravity on cosmological scales, but high-resolution N-body simulations of modified gravity (MG) are prohi…
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Andreas Filipp, Tri Nguyen, Laurence Perreault-Levasseur +5
Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally infer…