FV
Francisco Villaescusa-Navarro
astro-ph.COastro-ph.GAastro-ph.IMcs.LGhep-phcs.AIgr-qccs.CVstat.MLcs.CL
On Valency
published · living versionsW_ptazjfr8·v1 · currentpublished
VIDE: The Void IDentification and Examination toolkit
with P. M. Sutter, Guilhem Lavaux, Nico Hamaus, Alice Pisani +5
1 version
Preprints & journals
209 papers in the corpus · 2010–2026ENFR: Evidence Networks with Flow-Matched References for Amortized Simulation-Based Model Inference2610.23825v1 · Kai Lehman, Jameson Dong, Carolina Cuesta-Lazaro et al.2026 · 0 citationsarXiv
AI-assisted super-resolution cosmological simulations V: Cosmology-aware super-resolution2610.06710v1 · Xiaowen Zhang, Patrick Lachance, Rupert A.C. Croft et al.2026 · 0 citationsarXiv
EurekaBench: Measuring Agentic Ability to Discover New Scientific Insights2610.00492v1 · Jiayi Geng, Zhengxuan Wu, Kevin S. Chen et al.2026 · 0 citationsarXiv
FOTO: Figure-Level Semantic Search for Astronomy2609.30536v1 · Hurum Maksora Tohfa, Francisco Villaescusa-Navarro2026 · 0 citationsarXiv
The CAMELS-CROCODILE Simulation Suite: A New Cosmology--Astrophysics Playground for Machine Learning2609.23982v1 · Kentaro Nagamine, Yuri Oku, Atsushi J. Nishizawa et al.2026 · 0 citationsarXiv
Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs2609.09504v1 · James O. Baldwin, Shy Genel, Francisco Villaescusa-Navarro2026 · 0 citationsarXiv
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group2606.30855v2 · Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile et al.2026 · 0 citationsarXiv
AI Scientists as Engines of Discovery: A Case for Development within Reformed Institutions2606.22859v1 · Raul Jimenez, Boris Bolliet, Francisco Villaescusa-Navarro et al.2026 · 0 citationsarXiv
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes2606.10038v1 · Shy Genel, Yongseok Jo, Boon Kiat Oh et al.2026 · 1 citationarXiv
Reconciling the Fundamental Plane of Early-Type Galaxies with hydrodynamical simulations: The case of IllustrisTNG100-12605.31548v1 · Pedro de Araujo Ferreira, Nicola R. Napolitano, Crescenzo Tortora et al.2026 · 0 citationsarXiv
Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters2604.17981v2 · Christopher C. Lovell, Max E. Lee, William J. Roper et al.2026 · 0 citationsarXiv
Field-Level Inference of Primordial Non-Gaussianity with the Quijote Simulation Suite2603.20855v1 · Adam Andrews, Jens Jasche, Guilhem Lavaux et al.2026 · 0 citationsarXiv
Cosmology with one galaxy: An analytic formula relating with galaxy properties2602.07651v2 · Kito Liao, Francisco Villaescusa-Navarro, Romain Teyssier et al.2026 · 0 citationsarXiv
Cosmological back-reaction of baryons on dark matter in the CAMELS simulations2601.06258v1 · Matthew Gebhardt, Daniel Angl'es-Alc'azar, Shy Genel et al.2026 · 3 citationsarXiv
CAMELS Environments: The Impact of Local Neighbours on Galaxy Evolution across the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE Simulations2601.06290v1 · Xavier Sims, Daniel Angl'es-Alc'azar, Boon-Kiat Oh et al.2026 · 1 citationarXiv
Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models2512.10222v2 · Natal'i S. M. de Santi, Francisco Villaescusa-Navarro, Pablo Araya-Araya et al.2025 · 1 citationVolume 1007, Number 1, Year 2026, Pages 34
The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Density Profiles2512.03132v2 · Alex M. Garcia, Jonah C. Rose, Paul Torrey et al.2025 · 1 citationarXiv
The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Satellite Galaxies2512.02095v1 · Jonah C. Rose, Mariangela Lisanti, Paul Torrey et al.2025 · 0 citationsarXiv
The DREAMS Project: A New Suite of 1,024 Simulations to Contextualize the Milky Way and Assess Physics Uncertainties2512.00148v1 · Jonah C. Rose, Mariangela Lisanti, Paul Torrey et al.2025 · 0 citationsarXiv
Learning Cosmology from Nearest Neighbour Statistics2511.13393v2 · Atrideb Chatterjee, Arka Banerjee, Francisco Villaescusa-Navarro et al.2025 · 0 citationsarXiv
Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks2511.05367v1 · Ilem Leisher, Paul Torrey, Alex M. Garcia et al.2025 · 0 citationsarXiv
The Denario project: Deep knowledge AI agents for scientific discovery2510.26887v1 · Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo et al.2025 · 1 citationPRX Intelligence 1, 021001 (2026)
MG-NECOLA: Fast Neural Emulators for Modified Gravity Cosmologies2510.20086v1 · J. Bayron Orjuela-Quintana, Mauricio Reyes, Elena Giusarma et al.2025 · 0 citationsarXiv
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images2510.14766v1 · Andreas Filipp, Tri Nguyen, Laurence Perreault-Levasseur et al.2025 · 0 citationsarXiv
On the sensitivity of different galaxy properties to warm dark matter2510.05037v1 · Bel'en Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro et al.2025 · 2 citationsarXiv
One latent to fit them all: a unified representation of baryonic feedback on matter distribution2509.01881v3 · Shurui Lin, Yin Li, Shy Genel et al.2025 · 1 citationApJL 996 L41 (2026)
How does feedback affect the star formation histories of galaxies?2508.21152v1 · Kartheik G. Iyer, Tjitske K. Starkenburg, Greg L. Bryan et al.2025 · 7 citationsarXiv
CLAPP: The CLASS LLM Agent for Pair Programming2508.05728v1 · Santiago Casas, Christian Fidler, Boris Bolliet et al.2025 · 0 citationsarXiv
: Accurate non-Gaussian initial conditions with arbitrary bispectrum for N-body simulations2508.01855v1 · Emanuele Fondi, Licia Verde, Marco Baldi et al.2025 · 0 citationsarXiv
Evaluating Retrieval-Augmented Generation Agents for Autonomous Scientific Discovery in Astrophysics2507.07155v1 · Xueqing Xu, Boris Bolliet, Adrian Dimitrov et al.2025 · 0 citationsarXiv
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery2507.07257v2 · Licong Xu, Milind Sarkar, Anto I. Lonappan et al.2025 · 0 citationsarXiv
CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning2507.03707v2 · Ningyuan Huang, Richard Stiskalek, Jun-Young Lee et al.2025 · 0 citationsarXiv
Cosmology with Topological Deep Learning2505.23904v1 · Jun-Young Lee, Francisco Villaescusa-Navarro2025 · 3 citationsarXiv
Field-Level Comparison and Robustness Analysis of Cosmological N-body Simulations2505.13620v2 · Adrian E. Bayer, Francisco Villaescusa-Navarro, Sammy Sharief et al.2025 · 3 citationsApJ 989 207 (2025)
Interpreting Cosmological Information from Neural Networks in the Hydrodynamic Universe2504.17839v2 · Arnab Lahiry, Adrian E. Bayer, Francisco Villaescusa-Navarro2025 · 3 citationsAstrophys. J. 994 129 (2025)
Correcting for interloper contamination in the power spectrum with neural networks2504.06919v1 · Marina S. Cagliari, Azadeh Moradinezhad Dizgah, Francisco Villaescusa-Navarro2025 · 0 citationsarXiv
On the effects of parameters on galaxy properties in CAMELS and the predictability of 2503.22654v1 · Gabriella Contardo, Roberto Trotta, Serafina Di Gioia et al.2025 · 2 citationsAstrophys.J. 988 (2025) 1, 54
How many simulations do we need for simulation-based inference in cosmology?2503.13755v2 · Anirban Bairagi, Benjamin Wandelt, Francisco Villaescusa-Navarro2025 · 7 citationsA&A 703, A301 (2025)
Lost in the FoG: Pitfalls of Models for Large-Scale Hydrogen Distributions2503.01956v5 · Calvin Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro2025 · 1 citationarXiv
Cosmology with One Galaxy: Auto-Encoding the Galaxy Properties Manifold2502.17568v2 · Amanda Lue, Shy Genel, Marc Huertas-Company et al.2025 · 3 citationsThe Astrophysical Journal, 986(2), 133 (June 2025)
CASCO: Cosmological and AStrophysical parameters from Cosmological simulations and Observations III. The physics behind the emergence of the golden mass scale2502.13589v2 · C. Tortora, V. Busillo, N. R. Napolitano et al.2025 · 4 citationsA&A 702, A177 (2025)
Towards Robustness Across Cosmological Simulation Models TNG, SIMBA, ASTRID, and EAGLE2502.13239v1 · Yongseok Jo, Shy Genel, Anirvan Sengupta et al.2025 · 0 citationsarXiv
Learning the Universe: Tests of a Field Level -body Simulation Emulator2502.13242v1 · Matthew T. Scoggins, Matthew Ho, Francisco Villaescusa-Navarro et al.2025 · 0 citationsarXiv
Probing massive neutrinos and modified gravity with redshift-space morphologies and anisotropies of large-scale structure2412.05662v2 · Wei Liu, Liang Wu, Francisco Villaescusa-Navarro et al.2024 · 4 citationsarXiv
X-raying CAMELS: Constraining Baryonic Feedback in the Circum-Galactic Medium with the CAMELS simulations and eRASS X-ray Observations2412.04559v2 · Erwin T. Lau, Daisuke Nagai, 'Akos Bogd'an et al.2024 · 15 citations2025, ApJ, 984,190
Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours2411.13960v2 · Christopher C. Lovell, Tjitske Starkenburg, Matthew Ho et al.2024 · 12 citationsMon Not R Astron Soc (2025) 3949-3979
The constraining power of the Marked Power Spectrum: an analytical study2411.14377v1 · Marco Marinucci, Gabriel Jung, Michele Liguori et al.2024 · 5 citationsarXiv
Boosting HI-Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations2410.21225v2 · Eishica Chand, Arka Banerjee, Simon Foreman et al.2024 · 5 citationsarXiv
Quantifying Baryonic Feedback on Warm-Hot Circumgalactic Medium in CAMELS Simulations2410.16361v2 · Isabel Medlock, Chloe Neufeld, Daisuke Nagai (1 et al.2024 · 19 citationsarXiv
Cosmological and Astrophysical Parameter Inference from Stacked Galaxy Cluster Profiles Using CAMELS-zoomGZ2410.10942v1 · Elena Hern'andez-Mart'inez, Shy Genel, Francisco Villaescusa-Navarro et al.2024 · 4 citationsarXiv
Career total: at least 288 works. 209 are in this corpus.Showing the 50 most recent.
Profile built from the corpus for this byline.
Author records are still filling in while the Hub is in alpha. If this is your page, you'll be able to claim it soon. Spot a mistake? Tell us.