arXiv subjects

Francisco Villaescusa-Navarro

Publications and source records attributed to Francisco Villaescusa-Navarro.

At least 37 records · Page 2Linked to original sources

arXiv2026

Cosmological back-reaction of baryons on dark matter in the CAMELS simulations

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 redistribute dark matter due to changes in the gravitational potential. In this work, we investigate this "back-reaction" of baryons on dark matter using thousands of cosmological hydrodynamic simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, including parameter variations in the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE galaxy formation models. Matching haloes to corresponding N-body (dark matter-only) simulations, we find that virial masses decrease owing to the ejection of baryons by feedback. Relative to N-body simulations, halo profiles show an increased dark matter density in the center (due to radiative cooling) and a decrease in density farther out (due to feedback), with both effects being strongest in SIMBA (> 450% increase at r < 0.01 Rvir). The clustering of dark matter strongly responds to changes in baryonic physics, with dark matter power spectra in some simulations from each model showing as much as 20% suppression or increase in power at k ~ 10 h/Mpc relative to N-body simulations. We find that the dark matter back-reaction depends intrinsically on cosmology (Omega_m and sigma_8) at fixed baryonic physics, and varies strongly with the details of the feedback implementation. These results emphasize the need for marginalizing over uncertainties in baryonic physics to extract cosmological information from weak lensing surveys as well as their potential to constrain feedback models in galaxy evolution.

astro-ph.CO↗

arXiv2026

CAMELS Environments: The Impact of Local Neighbours on Galaxy Evolution across the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE Simulations

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. We investigate the impact of environment on galaxy evolution, and its dependence on baryonic physics implementation, using Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) spanning a wide range of stellar and AGN feedback implementations in the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE galaxy formation models. We show that satellite galaxies are significantly affected by the environment in all simulation models, with their gas fraction and star formation rate (SFR) suppressed in overdense regions compared to similar mass satellites in underdense environments at $z=0$. Central galaxies are less sensitive to environment but tend to show lower gas fraction and SFR in overdense regions at low stellar mass, transitioning to higher gas fraction and SFR for massive galaxies in higher-density environments. Halo baryon fraction ($f_{\rm B}$) and circumgalactic medium mass fraction ($f_{\rm CGM}$) at $z=0$ show clear environmental effects. In SIMBA, low-mass haloes in overdense regions have systematically lower $f_{\rm B}$ and $f_{\rm CGM}$ at fixed halo mass, while Swift-EAGLE haloes in overdense regions have systematically higher $f_{\rm B}$ and $f_{\rm CGM}$ across the full halo mass range, and IllustrisTNG and ASTRID show opposite trends at the low and high mass ends. Environmental effects can flip at higher redshift, with SFR and $f_{\rm B}$ increasing with local density in low-mass haloes before quenching at an increasing overdensity threshold. Our results demonstrate that the impact of environment on galaxy evolution depends significantly on galaxy formation model, and higher-density environments can either suppress or enhance star formation depending on galaxy mass and cosmic epoch.

astro-ph.GA↗

arXiv2025

The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Satellite Galaxies

We analyze the properties of satellite galaxies around 1,024 Milky Way-mass hosts from the DREAMS Project, simulated within a $Λ$CDM cosmology. Utilizing the TNG galaxy-formation model, the DREAMS simulations incorporate both baryonic physics and cosmological uncertainties for a large sample of galaxies with diverse environments and formation histories. We investigate the relative impact of the physical uncertainty from the galaxy-formation model on predicted satellite properties using four metrics: the satellite stellar mass function, radial distribution, inner slope of dark matter density profile, and stellar half-light radius. We compare these predictions to observations from the SAGA Survey and the DREAMS N-body simulations and find that uncertainties from baryonic physics modeling are subdominant to the scatter arising from halo-to-halo variance. Where baryonic modeling does affect satellites, the supernova wind energy has the largest effect on the satellite properties that we investigate. Specifically, increased supernova wind energy suppresses the stellar mass of satellites and results in more extended stellar half-light radii. The adopted wind speed has only a minor impact, and other astrophysical and cosmological parameters show no measurable effect. Our findings highlight the robustness of satellite properties against uncertainties in baryonic physics modeling.

astro-ph.GA↗

arXiv2025

The DREAMS Project: A New Suite of 1,024 Simulations to Contextualize the Milky Way and Assess Physics Uncertainties

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 simulation in the suite has a unique set of initial conditions and combination of cosmological and astrophysical parameters. The suite is designed to quantify theoretical uncertainties from halo-to-halo variance, as well as stellar and black hole feedback. We develop a novel weighting scheme that prioritizes regions of the input parameter space, yielding galaxies consistent with the observed present-day stellar mass--halo mass relation. The resulting galaxy population exhibits a wide diversity in structural properties that encompasses those of the actual Milky Way, providing a powerful statistical sample for galactic archaeology. To demonstrate the suite's scientific utility, we investigate the connection between a galaxy's merger history, focusing on Gaia-Sausage-Enceladus~(GSE) analogs, and its present-day properties. We find that galaxies with a GSE analog have lower star formation rates, more compact disks, and more spherical stellar halos. Crucially, significant halo-to-halo scatter remains, demonstrating that matching more than the most significant events in the Milky Way's past is necessary to recover its present-day properties. Our results highlight the necessity for large statistical samples to disentangle the stochastic nature of galaxy formation and robustly model the Milky Way's unique history.

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arXiv2025

Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks

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'' structure. Previous work has shown these merger trees are sensitive to cosmology simulation parameters, but as DM structures, the outstanding question of their sensitivity to DM models remains unanswered. In this work, we investigate the feasibility of deep learning methods trained on merger trees to infer Warm Dark Matter (WDM) particles masses from the DREAMS simulation suite. We organize the merger trees from 1,024 zoom-in simulations into graphs with nodes representing the merger history of galaxies and edges denoting hereditary links. We vary the complexity of the node features included in the graphs ranging from a single node feature up through an array of several galactic properties (e.g., halo mass, star formation rate, etc.). We train a Graph Neural Network (GNN) to predict the WDM mass using the graph representation of the merger tree as input. We find that the GNN can predict the mass of the WDM particle ($R^2$ from 0.07 to 0.95), with success depending on the graph complexity and node features. We extend the same methods to supernovae and active galactic nuclei feedback parameters $A_\text{SN1}$, $A_\text{SN2}$, and $A_\text{AGN}$, successfully inferring the supernovae parameters. The GNN can even infer the WDM mass from merger tree histories without any node features, indicating that the structure of merger trees alone inherits information about the cosmological parameters of the simulations from which they form.

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arXiv2025

CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning

Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed light on the nature and composition of the Universe. In this paper we introduce CosmoBench, a benchmark dataset curated from state-of-the-art cosmological simulations whose runs required more than 41 million core-hours and generated over two petabytes of data. CosmoBench is the largest dataset of its kind: it contains 34 thousand point clouds from simulations of dark matter halos and galaxies at three different length scales, as well as 25 thousand directed trees that record the formation history of halos on two different time scales. The data in CosmoBench can be used for multiple tasks -- to predict cosmological parameters from point clouds and merger trees, to predict the velocities of individual halos and galaxies from their collective positions, and to reconstruct merger trees on finer time scales from those on coarser time scales. We provide several baselines on these tasks, some based on established approaches from cosmological modeling and others rooted in machine learning. For the latter, we study different approaches -- from simple linear models that are minimally constrained by symmetries to much larger and more computationally-demanding models in deep learning, such as graph neural networks. We find that least-squares fits with a handful of invariant features sometimes outperform deep architectures with many more parameters and far longer training time. Still there remains tremendous potential to improve these baselines by combining machine learning and cosmology to fully exploit the data. CosmoBench sets the stage for bridging cosmology and geometric deep learning at scale. We invite the community to push the frontier of scientific discovery by engaging with this dataset, available at https://cosmobench.streamlit.app

cs.LG↗

arXiv2025

The Denario project: Deep knowledge AI agents for scientific discovery

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 literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

cs.AI↗

arXiv2025

Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours

We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference approach. Using the Synthesizer code we simulate the dust attenuated ultraviolet-near infrared stellar emission from galaxies in thousands of cosmological hydrodynamic simulations from the CAMELS suite, including the Swift-EAGLE, IllustrisTNG, Simba & Astrid galaxy formation models. For each galaxy we calculate the rest-frame luminosity in a number of photometric bands, including the SDSS $\textit{ugriz}$ and GALEX FUV & NUV filters; this dataset represents the largest catalogue of synthetic photometry based on hydrodynamic galaxy formation simulations produced to date, totalling >200 million sources. From these we compile luminosity functions and colour distributions, and find clear dependencies on both cosmology and feedback. We then perform simulation based (likelihood-free) inference using these distributions to constrain $Ω_m$, $σ_8$, and four parameters controlling the strength of stellar and AGN feedback. Both colour distributions and luminosity functions provide complementary information on certain parameters when performing inference. We achieve constraints on the stellar feedback parameters, as well as $Ω_m$ and $σ_8$. The latter is attributable to the fact that the photometry encodes the star formation-metal enrichment history of each galaxy; galaxies in a universe with a higher $σ_8$ tend to form earlier and have higher metallicities, which leads to redder colours. We find that a model trained on one galaxy formation simulation generalises poorly when applied to another, and attribute this to differences in the subgrid prescriptions, and lack of flexibility in our emission modelling. The photometric catalogues are publicly available at: https://camels.readthedocs.io/

astro-ph.GA↗

arXiv2025

Lost in the FoG: Pitfalls of Models for Large-Scale Hydrogen Distributions

Large-scale HI surveys and their cross-correlations with galaxy distributions have immense potential as cosmological probes. Interpreting these measurements requires theoretical models that must incorporate redshift-space distortions (RSDs), such as the Kaiser and fingers-of-God (FoG) effect, and differences in the tracer and matter distributions via the tracer bias. These effects are commonly approximated with assumptions that should be tested on simulated distributions. In this work, we use the hydrodynamical simulation suite IllustrisTNG to assess the performance of models of $z \leq 1$ HI auto and HI-galaxy cross-power spectra, finding that the models employed by recent observations introduce errors comparable to or exceeding their measurement uncertainties. In particular, neglecting FoG causes $\gtrsim 10\%$ deviations between the modeled and simulated power spectra at $k \gtrsim 0.1$ $h$ / Mpc, larger than assuming a constant bias which reaches the same error threshold at slightly smaller scales. However, even without these assumptions, models can still err by $\sim 10\%$ on relevant scales. These remaining errors arise from multiple RSD damping sources on HI clustering, which are not sufficiently described with a single FoG term. Overall, our results highlight the need for an improved understanding of RSDs to harness the capabilities of future measurements of HI distributions.

astro-ph.CO↗

arXiv2025

MG-NECOLA: Fast Neural Emulators for Modified Gravity Cosmologies

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 prohibitively expensive. We present MG-NECOLA, a convolutional neural network that enhances fast MG-PICOLA simulations to near-N-body fidelity at a fraction of the cost. MG-NECOLA reproduces QUIJOTE-MG N-body results in the power spectrum and bispectrum with better than 1% accuracy down to non-linear scales ($k \simeq 1~h~\mathrm{Mpc}^{-1}$), while reducing computational time by several orders of magnitude. Importantly, although trained only on $f(R)$ models with massless neutrinos, the network generalizes robustly to scenarios with massive neutrinos, preserving accuracy to within 5% at non-linear scales. This combination of precision and robustness establishes MG-NECOLA as a practical emulator for producing large ensembles of high-fidelity simulations, enabling efficient exploration of modified gravity and beyond-$Λ$CDM cosmologies in upcoming surveys.

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arXiv2025

Predicting the Subhalo Mass Functions in Simulations from Galaxy Images

Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as the predicted SHMF can vary significantly between galaxies - even within the same cosmological model - due to differences in the properties and environment of individual galaxies. We present a machine learning framework to infer the galaxy-specific predicted SHMF from galaxy images, conditioned on the assumed inverse warm DM particle mass $M^{-1}_{\rm DM}$. To train the model, we use 1024 high-resolution hydrodynamical zoom-in simulations from the DREAMS suite. Mock observations are generated using Synthesizer, excluding gas particle contributions, and SHMFs are computed with the Rockstar halo finder. Our neural network takes as input both the galaxy images and the inverse DM mass. This method enables scalable, image-based predictions for the theoretical DM SHMFs of individual galaxies, facilitating direct comparisons with observational measurements.

astro-ph.CO↗

arXiv2025

Cosmology with One Galaxy: Auto-Encoding the Galaxy Properties Manifold

Cosmological simulations like CAMELS and IllustrisTNG characterize hundreds of thousands of galaxies using various internal properties. Previous studies have demonstrated that machine learning can be used to infer the cosmological parameter $Ω_m$ from the internal properties of even a single randomly selected simulated galaxy. This ability was hypothesized to originate from galaxies occupying a low-dimensional manifold within a higher-dimensional galaxy property space, which shifts with variations in $Ω_m$. In this work, we investigate how galaxies occupy the high-dimensional galaxy property space, particularly the effect of $Ω_m$ and other cosmological and astrophysical parameters on the putative manifold. We achieve this by using an autoencoder with an Information-Ordered Bottleneck (IOB), a neural layer with adaptive compression, to perform dimensionality reduction on individual galaxy properties from CAMELS simulations, which are run with various combinations of cosmological and astrophysical parameters. We find that for an autoencoder trained on the fiducial set of parameters, the reconstruction error increases significantly when the test set deviates from fiducial values of $Ω_m$ and $A_{\text{SN1}}$, indicating that these parameters shift galaxies off the fiducial manifold. In contrast, variations in other parameters such as $σ_8$ cause negligible error changes, suggesting galaxies shift along the manifold. These findings provide direct evidence that the ability to infer $Ω_m$ from individual galaxies is tied to the way $Ω_m$ shifts the manifold. Physically, this implies that parameters like $σ_8$ produce galaxy property changes resembling natural scatter, while parameters like $Ω_m$ and $A_{\text{SN1}}$ create unsampled properties, extending beyond the natural scatter in the fiducial model.

astro-ph.CO↗

arXiv2025

On the sensitivity of different galaxy properties to warm dark matter

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 begin by using a Multilayer Perceptron (MLP) coupled with a normalizing flow to explore global statistical descriptors of galaxy populations, such as the mean, standard deviation, and histograms of 14 galaxy properties. We find that subhalo gas mass is the most informative feature for constraining the WDM mass, achieving a determination coefficient of R^2 = 0.9. We employ symbolic regression to extract simple, interpretable relations with the WDM particle mass. Finally, we adopt a more localized approach by selecting individual dark matter halos and using a Graph Neural Network (GNN) with a normalizing flow to infer the WDM mass, incorporating subhalo properties as node features and global simulation statistics as graph-level features. The GNN approach yields only a residual improvement over MLP models based solely on global features, indicating that most of the predictive power resides in the global descriptors, with only marginal gains from halo-level information.

astro-ph.CO↗

arXiv2025

How many simulations do we need for simulation-based inference in cosmology?

How many simulations do we need to train machine learning methods to extract information available from summary statistics of the cosmological density field? Neural methods have shown the potential to extract non-linear information available from cosmological data. Success depends critically on having sufficient simulations for training the networks and appropriate network architectures. In the first detailed convergence study of neural network training for cosmological inference, we show that currently available simulation suites, such as the Quijote Latin Hypercube(LH) with 2000 simulations, do not provide sufficient training data for a generic neural network to reach the optimal regime, even for the dark matter power spectrum, and in an idealized case. We discover an empirical neural scaling law that predicts how much information a neural network can extract from a highly informative summary statistic, the dark matter power spectrum, as a function of the number of simulations used to train the network, for a wide range of architectures and hyperparameters. We combine this result with the Cramer-Rao information bound to forecast the number of training simulations needed for near-optimal information extraction. To verify our method we created the largest publicly released simulation data set in cosmology, the Big Sobol Sequence(BSQ), consisting of 32,768 $Λ$CDM n-body simulations uniformly covering the $Λ$CDM parameter space. Our method enables efficient planning of simulation campaigns for machine learning applications in cosmology, while the BSQ dataset provides an unprecedented resource for studying the convergence behavior of neural networks in cosmological parameter inference. Our results suggest that new large simulation suites or new training approaches will be necessary to achieve information-optimal parameter inference from non-linear simulations.

astro-ph.CO↗

arXiv2025

How does feedback affect the star formation histories of galaxies?

Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the CAMELS suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at $z\sim0$ across the IllustrisTNG, SIMBA and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affects the efficiency of baryon cycling and the rates at which central black holes grow, while effects of varying AGN feedback depend on model-dependent implementations of black hole seeding, accretion and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power-law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.

astro-ph.GA↗

arXiv2025

Field-Level Comparison and Robustness Analysis of Cosmological N-body Simulations

We present the first field-level comparison of cosmological N-body simulations, considering various widely used codes: Abacus, CUBEP$^3$M, Enzo, Gadget, Gizmo, PKDGrav, and Ramses. Unlike previous comparisons focused on summary statistics, we conduct a comprehensive field-level analysis: evaluating statistical similarity, quantifying implications for cosmological parameter inference, and identifying the regimes in which simulations are consistent. We begin with a traditional comparison using the power spectrum, cross-correlation coefficient, and visual inspection of the matter field. We follow this with a statistical out-of-distribution (OOD) analysis to quantify distributional differences between simulations, revealing insights not captured by the traditional metrics. We then perform field-level simulation-based inference (SBI) using convolutional neural networks (CNNs), training on one simulation and testing on others, including a full hydrodynamic simulation for comparison. We identify several causes of OOD behavior and biased inference, finding that resolution effects, such as those arising from adaptive mesh refinement (AMR), have a significant impact. Models trained on non-AMR simulations fail catastrophically when evaluated on AMR simulations, introducing larger biases than those from hydrodynamic effects. Differences in resolution, even when using the same N-body code, likewise lead to biased inference. We attribute these failures to a CNN's sensitivity to small-scale fluctuations, particularly in voids and filaments, and demonstrate that appropriate smoothing brings the simulations into statistical agreement. Our findings motivate the need for careful data filtering and the use of field-level OOD metrics, such as PQMass, to ensure robust inference.

astro-ph.CO↗

arXiv2025

CLAPP: The CLASS LLM Agent for Pair Programming

We introduce CLAPP (CLASS LLM Agent for Pair Programming), an interactive AI assistant designed to support researchers working with the Einstein-Boltzmann solver CLASS. CLAPP leverages large language models (LLMs) and domain-specific retrieval to provide conversational coding support for CLASS-answering questions, generating code, debugging errors, and producing plots. Its architecture combines multi-agent LLM orchestration, semantic search across CLASS documentation, and a live Python execution environment. Deployed as a user-friendly web application, CLAPP lowers the entry barrier for scientists unfamiliar with AI tools and enables more productive human-AI collaboration in computational and numerical cosmology. The app is available at https://classclapp.streamlit.app

astro-ph.IM↗

arXiv2025

$\texttt{GENGARS}$: Accurate non-Gaussian initial conditions with arbitrary bispectrum for N-body simulations

Primordial non-Gaussianity is predicted by various inflationary models, and N-body simulations are a crucial tool for studying its imprints on large-scale structure. In this work, we present \texttt{GENGARS} ( GEnerator of Non-Gaussian ARbitrary Shapes), a framework for generating accurate non-Gaussian initial conditions for N-body simulations. It builds upon the formulation introduced by Wagner \& Verde (2012), enabling to generate a primordial gravitational potential with a desired separable bispectrum $B_Φ(k_1,k_2,k_3)$. For the local, equilateral and orthogonal non-Gaussian templates, we benchmark our method against the well-established \texttt{2LPT-PNG} code. We show that \texttt{GENGARS} achieves improved accuracy and lower noise by suppressing spurious contributions to the primordial power spectrum. This paper aims at presenting the method, quantifying its performance and illustrating the benefits and applicable use cases over existing approaches.

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