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Francisco Villaescusa-Navarro

Publications and source records attributed to Francisco Villaescusa-Navarro.

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arXiv2023

Cosmology with Galaxy Photometry Alone

We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. (2022; arXiv:2201.02202) recently demonstrated that the internal physical properties of a single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g. optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of $\sim$20,000 NASA-Sloan Atlas galaxies, we constrain $Ω_m = 0.323^{+0.075}_{-0.095}$ and $σ_8 = 0.799^{+0.088}_{-0.085}$.

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arXiv2023

Machine-learning cosmology from void properties

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 Universe. We show that machine learning techniques can unlock the information in void features for cosmological parameter inference. We rely on thousands of void catalogs from the GIGANTES dataset, where every catalog contains an average of 11,000 voids from a volume of $1~(h^{-1}{\rm Gpc})^3$. We focus on three properties of cosmic voids: ellipticity, density contrast, and radius. We train 1) fully connected neural networks on histograms from individual void properties and 2) deep sets from void catalogs, to perform likelihood-free inference on the value of cosmological parameters. We find that our best models are able to constrain the value of $Ω_{\rm m}$, $σ_8$, and $n_s$ with mean relative errors of $10\%$, $4\%$, and $3\%$, respectively, without using any spatial information from the void catalogs. Our results provide an illustration for the use of machine learning to constrain cosmology with voids.

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arXiv2023

Predicting the impact of feedback on matter clustering with machine learning in CAMELS

Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. We investigate the impact of baryonic physics on matter clustering at $z=0$ using a library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, containing thousands of $(25\,h^{-1}{\rm Mpc})^3$ volume realizations with varying cosmology, initial random field, stellar and AGN feedback strength and sub-grid model implementation methods. We show that baryonic physics affects matter clustering on scales $k \gtrsim 0.4\,h\,\mathrm{Mpc}^{-1}$ and the magnitude of this effect is dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive halos ($M_{\rm 200c} > 10^{13.5}$\,\Msun) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of halos across the full mass range $10^{10} \leq M_\mathrm{halo}/$\Msun$< 10^{15}$ can predict the effect of galaxy formation on the matter power spectrum on scales $k = 1.0$--20.0\,$h\,\mathrm{Mpc}^{-1}$.

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arXiv2023

Cosmology with multiple galaxies

Recent works have discovered a relatively tight correlation between $Ω_{\rm m}$ and properties of individual simulated galaxies. Because of this, it has been shown that constraints on $Ω_{\rm m}$ can be placed using the properties of individual galaxies while accounting for uncertainties on astrophysical processes such as feedback from supernova and active galactic nuclei. In this work, we quantify whether using the properties of multiple galaxies simultaneously can tighten those constraints. For this, we train neural networks to perform likelihood-free inference on the value of two cosmological parameters ($Ω_{\rm m}$ and $σ_8$) and four astrophysical parameters using the properties of several galaxies from thousands of hydrodynamic simulations of the CAMELS project. We find that using properties of more than one galaxy increases the precision of the $Ω_{\rm m}$ inference. Furthermore, using multiple galaxies enables the inference of other parameters that were poorly constrained with one single galaxy. We show that the same subset of galaxy properties are responsible for the constraints on $Ω_{\rm m}$ from one and multiple galaxies. Finally, we quantify the robustness of the model and find that without identifying the model range of validity, the model does not perform well when tested on galaxies from other galaxy formation models.

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arXiv2023

Learning neutrino effects in Cosmology with Convolutional Neural Networks

Measuring the sum of the three active neutrino masses, $M_ν$, is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables in particular on the large-scale structure of the Universe. In order to maximize the information that can be retrieved from galaxy surveys, accurate theoretical predictions in the non-linear regime are needed. Currently, one way to achieve those predictions is by running cosmological numerical simulations. Unfortunately, producing those simulations requires high computational resources -- several hundred to thousand core-hours for each neutrino mass case. In this work, we propose a new method, based on a deep learning network, to quickly generate simulations with massive neutrinos from standard $Λ$CDM simulations without neutrinos. We computed multiple relevant statistical measures of deep-learning generated simulations, and conclude that our approach is an accurate alternative to the traditional N-body techniques. In particular the power spectrum is within $\simeq 6\%$ down to non-linear scales $k=0.7$~\rm h/Mpc. Finally, our method allows us to generate massive neutrino simulations 10,000 times faster than the traditional methods.

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arXiv2023

Predicting Interloper Fraction with Graph Neural Networks

Upcoming emission-line spectroscopic surveys, such as Euclid and the Roman Space Telescope, will be affected by systematic effects due to the presence of interlopers: galaxies whose redshift and distance from us are miscalculated due to line confusion in their emission spectra. Particularly pernicious are interlopers involving the confusion between two lines with close emitted wavelengths, like H$β$ emitters confused as \oiii, since those are strongly spatially correlated with the target galaxies. They introduce a particular pattern in the 3D distribution of the observed galaxy catalog that can shift the position of the BAO peak in the galaxy correlation function and bias any cosmological analysis performed with that sample. Here we present a novel method to predict the fraction of interlopers in a galaxy catalog, using Graph Neural Networks (GNNs) to learn the posterior distribution of the interloper fraction while marginalizing over cosmology and galaxy bias. The method is developed using simulations with halos acting as a proxy for galaxies. The GNN can infer the mean and standard deviation of the posterior distribution of interloper fraction using small-scale information that is usually not considered in cosmological analyses. The injection of large-scale information into the graph as a global attribute improves the performance of the GNN when marginalizing over cosmology.

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arXiv2023

Emulating Radiative Transfer with Artificial Neural Networks

Forward-modeling observables from galaxy simulations enables direct comparisons between theory and observations. To generate synthetic spectral energy distributions (SEDs) that include dust absorption, re-emission, and scattering, Monte Carlo radiative transfer is often used in post-processing on a galaxy-by-galaxy basis. However, this is computationally expensive, especially if one wants to make predictions for suites of many cosmological simulations. To alleviate this computational burden, we have developed a radiative transfer emulator using an artificial neural network (ANN), ANNgelina, that can reliably predict SEDs of simulated galaxies using a small number of integrated properties of the simulated galaxies: star formation rate, stellar and dust masses, and mass-weighted metallicities of all star particles and of only star particles with age <10 Myr. Here, we present the methodology and quantify the accuracy of the predictions. We train the ANN on SEDs computed for galaxies from the IllustrisTNG project's TNG50 cosmological magnetohydrodynamical simulation. ANNgelina is able to predict the SEDs of TNG50 galaxies in the ultraviolet (UV) to millimetre regime with a typical median absolute error of ~7 per cent. The prediction error is the greatest in the UV, possibly due to the viewing-angle dependence being greatest in this wavelength regime. Our results demonstrate that our ANN-based emulator is a promising computationally inexpensive alternative for forward-modeling galaxy SEDs from cosmological simulations.

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arXiv2023

Cosmological baryon spread and impact on matter clustering in CAMELS

We quantify the cosmological spread of baryons relative to their initial neighboring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighboring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighboring dark matter owing to feedback from supernovae (SNe) and Active Galactic Nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial \textsc{SIMBA} model spreading $\sim$40\% of baryons $>$1\,Mpc away compared to $\sim$10\% for the IllustrisTNG and \textsc{ASTRID} models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired $N$-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number ($k$) and baryonic spread up to $k \sim 10\,h$\,Mpc$^{-1}$ while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation.

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arXiv2023

Robust Field-level Likelihood-free Inference with Galaxies

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 models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain $3$D positions and radial velocities of $\sim 1, 000$ galaxies in tiny $(25~h^{-1}{\rm Mpc})^3$ volumes our models can infer the value of $Ω_{\rm m}$ with approximately $12$ % precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and AGN feedback, run with five different codes and subgrid models - IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE -, we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on $1,024$ simulations that cover a vast region in parameter space - variations in $5$ cosmological and $23$ astrophysical parameters - finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than $\sim10~h^{-1}{\rm kpc}$.

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arXiv2023

A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship

Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a normalizing flow (NF) to map the probability of various galaxy and halo properties conditioned on astrophysical and cosmological parameters. By leveraging the learnt conditional relationships we can explore a wide range of interesting questions, whilst enabling simple marginalisation over nuisance parameters. We demonstrate how the model can be used as a generative model for arbitrary values of our conditional parameters; we generate halo masses and matched galaxy properties, and produce realisations of the halo mass function as well as a number of galaxy scaling relations and distribution functions. The model represents a unique and flexible approach to modelling the galaxy-halo relationship.

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arXiv2023

Signatures of a Parity-Violating Universe

What would a parity-violating universe look like? We present a numerical and theoretical study of mirror asymmetries in the late universe, using a new suite of $N$-body simulations: QUIJOTE-Odd. These feature parity-violating initial conditions, injected via a simple ansatz for the imaginary primordial trispectrum and evolved into the non-linear regime. We find that the realization-averaged power spectrum, bispectrum, halo mass function, and matter PDF are not affected by our modifications to the initial conditions, deep into the non-linear regime, which we argue arises from rotational and translational invariance. In contrast, the parity-odd trispectrum of matter (measured using a new estimator), shows distinct signatures proportional to the parity-violating parameter, $p_{\rm NL}$, which sets the amplitude of the primordial trispectrum. We additionally find intriguing signatures in the angular momentum of halos, with the primordial trispectrum inducing a non-zero correlation between angular momentum and smoothed velocity field, proportional to $p_{\rm NL}$. Our simulation suite has been made public to facilitate future analyses.

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arXiv2023

Quijote-PNG: Simulations of primordial non-Gaussianity and the information content of the matter field power spectrum and bispectrum

Primordial non-Gaussianity (PNG) is one of the most powerful probes of the early Universe and measurements of the large scale structure of the Universe have the potential to transform our understanding of this area. However relating measurements of the late time Universe to the primordial perturbations is challenging due to the non-linear processes that govern the evolution of the Universe. To help address this issue we release a large suite of N-body simulations containing four types of PNG: \textsc{quijote-png}. These simulations were designed to augment the \textsc{quijote} suite of simulations that explored the impact of various cosmological parameters on large scale structure observables. Using these simulations we investigate how much information on PNG can be extracted by extending power spectrum and bispectrum measurements beyond the perturbative regime at $z=0.0$. This is the first joint analysis of the PNG and cosmological information content accessible with power spectrum and bispectrum measurements of the non-linear scales. We find that the constraining power improves significantly up to $k_\mathrm{max}\approx 0.3 h/{\rm Mpc}$, with diminishing returns beyond as the statistical probes signal-to-noise ratios saturate. This saturation emphasizes the importance of accurately modelling all the contributions to the covariance matrix. Further we find that combining the two probes is a powerful method of breaking the degeneracies with the $Λ$CDM parameters.

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arXiv2023

Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but must be trained carefully on large and representative data sets. We developed and generated a new `hump' of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100 $h^{-1}$ cMpc)$^3$ with different cosmological parameters ($Ω_m$ and $σ_8$) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof-of-concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation function, count-in-cells, and the Void Probability Function, and probe non-linear and linear scales across $0.68<$ R $<27\ h^{-1}$ cMpc. Our cosmological constraints cluster around 3-8$\%$ error on $Ω_{\text{M}}$ and $σ_8$, and we explore the effect of various galaxy selections, galaxy sampling, and choice of clustering statistics on these constraints. We additionally explore how these clustering statistics constrain and inform key stellar and galactic feedback parameters in the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.

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arXiv2023

Quijote-PNG: Quasi-maximum likelihood estimation of Primordial Non-Gaussianity in the non-linear halo density field

We study primordial non-Gaussian signatures in the redshift-space halo field on non-linear scales, using a quasi-maximum likelihood estimator based on optimally compressed power spectrum and modal bispectrum statistics. We train and validate the estimator on a suite of halo catalogues constructed from the Quijote-PNG N-body simulations, which we release to accompany this paper. We verify its unbiasedness and near optimality, for the three main types of primordial non-Gaussianity (PNG): local, equilateral, and orthogonal. We compare the modal bispectrum expansion with a $k$-binning approach, showing that the former allows for faster convergence of numerical derivatives in the computation of the score-function, thus leading to better final constraints. We find, in agreement with previous studies, that the local PNG signal in the halo-field is dominated by the scale-dependent bias signature on large scales and saturates at $k \sim 0.2~h\,\mathrm{Mpc}^{-1}$, whereas the small-scale bispectrum is the main source of information for equilateral and orthogonal PNG. Combining power spectrum and bispectrum on non-linear scales plays an important role in breaking degeneracies between cosmological and PNG parameters; such degeneracies remain however strong for equilateral PNG. We forecast that PNG parameters can be constrained with $Δf_\mathrm{NL}^\mathrm{local} = 45$, $Δf_\mathrm{NL}^\mathrm{equil} = 570$, $Δf_\mathrm{NL}^\mathrm{ortho} = 110$, on a cubic volume of $1 \left({ {\rm Gpc}/{ {\rm h}}} \right)^3$, at $z = 1$, considering scales up to $k_\mathrm{max} = 0.5~h\,\mathrm{Mpc}^{-1}$.

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arXiv2023

The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback

Feedback from active galactic nuclei (AGN) and supernovae can affect measurements of integrated SZ flux of halos ($Y_\mathrm{SZ}$) from CMB surveys, and cause its relation with the halo mass ($Y_\mathrm{SZ}-M$) to deviate from the self-similar power-law prediction of the virial theorem. We perform a comprehensive study of such deviations using CAMELS, a suite of hydrodynamic simulations with extensive variations in feedback prescriptions. We use a combination of two machine learning tools (random forest and symbolic regression) to search for analogues of the $Y-M$ relation which are more robust to feedback processes for low masses ($M\lesssim 10^{14}\, h^{-1} \, M_\odot$); we find that simply replacing $Y\rightarrow Y(1+M_*/M_\mathrm{gas})$ in the relation makes it remarkably self-similar. This could serve as a robust multiwavelength mass proxy for low-mass clusters and galaxy groups. Our methodology can also be generally useful to improve the domain of validity of other astrophysical scaling relations. We also forecast that measurements of the $Y-M$ relation could provide percent-level constraints on certain combinations of feedback parameters and/or rule out a major part of the parameter space of supernova and AGN feedback models used in current state-of-the-art hydrodynamic simulations. Our results can be useful for using upcoming SZ surveys (e.g., SO, CMB-S4) and galaxy surveys (e.g., DESI and Rubin) to constrain the nature of baryonic feedback. Finally, we find that the an alternative relation, $Y-M_*$, provides complementary information on feedback than $Y-M$

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arXiv2023

Inferring Warm Dark Matter Masses with Deep Learning

We present a new suite of over 1,500 cosmological N-body simulations with varied Warm Dark Matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses from images of DM field data. Our fiducial setup can make accurate predictions of the WDM particle mass up to 7.5 keV at a 95% confidence level from small maps that cover an area of (25 h$^{-1}$ Mpc)$^2$. We vary the image resolution, simulation resolution, redshift, and cosmology of our fiducial setup to better understand how our model is making predictions. Using these variations, we find that our models are most dependent on simulation resolution, minimally dependent on image resolution, not systematically dependent on redshift, and robust to varied cosmologies. We also find that an important feature to distinguish between WDM models is present with a linear size between 100 and 200 h$^{-1}$ kpc. We compare our fiducial model to one trained on the power spectrum alone and find that our field-level model can make 2x more precise predictions and can make accurate predictions to 2x as massive WDM particle masses when used on the same data. Overall, we find that the field-level data can be used to accurately differentiate between WDM models and contain more information than is captured by the power spectrum. This technique can be extended to more complex DM models and opens up new opportunities to explore alternative DM models in a cosmological environment.

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arXiv2023

Cosmology with one galaxy? -- The ASTRID model and robustness

Recent work has pointed out the potential existence of a tight relation between the cosmological parameter $Ω_{\rm m}$, at fixed $Ω_{\rm b}$, and the properties of individual galaxies in state-of-the-art cosmological hydrodynamic simulations. In this paper, we investigate whether such a relation also holds for galaxies from simulations run with a different code that made use of a distinct subgrid physics: Astrid. We find that also in this case, neural networks are able to infer the value of $Ω_{\rm m}$ with a $\sim10\%$ precision from the properties of individual galaxies while accounting for astrophysics uncertainties as modeled in CAMELS. This tight relationship is present at all considered redshifts, $z\leq3$, and the stellar mass, the stellar metallicity, and the maximum circular velocity are among the most important galaxy properties behind the relation. In order to use this method with real galaxies, one needs to quantify its robustness: the accuracy of the model when tested on galaxies generated by codes different from the one used for training. We quantify the robustness of the models by testing them on galaxies from four different codes: IllustrisTNG, SIMBA, Astrid, and Magneticum. We show that the models perform well on a large fraction of the galaxies, but fail dramatically on a small fraction of them. Removing these outliers significantly improves the accuracy of the models across simulation codes.

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arXiv2023

The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites

We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2,124 hydrodynamic simulation runs that vary 3 cosmological parameters ($Ω_m$, $σ_8$, $Ω_b$) and 4 parameters controlling stellar and AGN feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex non-linear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set.

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