arXiv subjects

Francisco Villaescusa-Navarro

Publications and source records attributed to Francisco Villaescusa-Navarro.

At least 73 records · Page 4Linked to original sources

arXiv2024

How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds

The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upcoming cosmological surveys, we need an accurate way to model this complex relationship. Many techniques have been developed to model this connection, from Halo Occupation Distribution (HOD) to empirical and semi-analytic models to hydrodynamic. Hydrodynamic simulations can incorporate more detailed astrophysical processes but are computationally expensive; HODs, on the other hand, are computationally cheap but have limited accuracy. In this work, we present NeHOD, a generative framework based on variational diffusion model and Transformer, for painting galaxies/subhalos on top of DM with an accuracy of hydrodynamic simulations but at a computational cost similar to HOD. By modeling galaxies/subhalos as point clouds, instead of binning or voxelization, we can resolve small spatial scales down to the resolution of the simulations. For each halo, NeHOD predicts the positions, velocities, masses, and concentrations of its central and satellite galaxies. We train NeHOD on the TNG-Warm DM suite of the DREAMS project, which consists of 1024 high-resolution zoom-in hydrodynamic simulations of Milky Way-mass halos with varying warm DM mass and astrophysical parameters. We show that our model captures the complex relationships between subhalo properties as a function of the simulation parameters, including the mass functions, stellar-halo mass relations, concentration-mass relations, and spatial clustering. Our method can be used for a large variety of downstream applications, from galaxy clustering to strong lensing studies.

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arXiv2024

Field-level Emulation of Cosmic Structure Formation with Cosmology and Redshift Dependence

We present a field-level emulator for large-scale structure, capturing the cosmology dependence and the time evolution of cosmic structure formation. The emulator maps linear displacement fields to their corresponding nonlinear displacements from N-body simulations at specific redshifts. Designed as a neural network, the emulator incorporates style parameters that encode dependencies on $Ω_{\rm m}$ and the linear growth factor $D(z)$ at redshift $z$. We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model's displacement outputs. This innovation results in significant improvements in training efficiency and model accuracy. Tested on diverse cosmologies and redshifts not seen during training, the emulator achieves percent-level accuracy on scales of $k\sim~1~{\rm Mpc}^{-1}~h$ at $z=0$, with improved performance at higher redshifts. We compare predicted structure formation histories with N-body simulations via merger trees, finding consistent merger event sequences and statistical properties.

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arXiv2024

Towards unveiling the large-scale nature of gravity with the wavelet scattering transform

We present the first application of the Wavelet Scattering Transform (WST) in order to constrain the nature of gravity using the three-dimensional (3D) large-scale structure of the universe. Utilizing the Quijote-MG N-body simulations, we can reliably model the 3D matter overdensity field for the f(R) Hu-Sawicki modified gravity (MG) model down to $k_{\rm max}=0.5$ h/Mpc. Combining these simulations with the Quijote $ν$CDM collection, we then conduct a Fisher forecast of the marginalized constraints obtained on gravity using the WST coefficients and the matter power spectrum at redshift z=0. Our results demonstrate that the WST substantially improves upon the 1$σ$ error obtained on the parameter that captures deviations from standard General Relativity (GR), yielding a tenfold improvement compared to the corresponding matter power spectrum result. At the same time, the WST also enhances the precision on the $Λ$CDM parameters and the sum of neutrino masses, by factors of 1.2-3.4 compared to the matter power spectrum, respectively. Despite the overall reduction in the WST performance when we focus on larger scales, it still provides a relatively $4.5\times$ tighter 1$σ$ error for the MG parameter at $k_{\rm max}=0.2$ h/Mpc, highlighting its great sensitivity to the underlying gravity theory. This first proof-of-concept study reaffirms the constraining properties of the WST technique and paves the way for exciting future applications in order to perform precise large-scale tests of gravity with the new generation of cutting-edge cosmological data.

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arXiv2024

Cosmological simulations of scale-dependent primordial non-Gaussianity

We present the results of a set of cosmological N-body simulations with standard $Λ$CDM cosmology but characterized by a scale-dependent primordial non-Gaussianity of the local type featuring a power-law dependence of the $f_{\rm NL}^{\rm loc}(k)$ at large scales followed by a saturation to a constant value at smaller scales where non-linear growth leads to the formation of collapsed cosmic structures. Such models are built to ensure consistency with current Cosmic Microwave Background bounds on primordial non-Gaussianity yet allowing for large effects of the non-Gaussian statistics on the properties of non-linear structure formation. We show the impact of such scale-dependent non-Gaussian scenarios on a wide range of properties of the resulting cosmic structures, such as the non-linear matter power spectrum, the halo and sub-halo mass functions, the concentration-mass relation, the halo and void density profiles, and we highlight for the first time that some of these models might mimic the effects of Warm Dark Matter for several of such observables

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arXiv2024

Probing the Circum-Galactic Medium with Fast Radio Bursts: Insights from the CAMELS Simulations

Most diffuse baryons, including the circumgalactic medium (CGM) surrounding galaxies and the intergalactic medium (IGM) in the cosmic web, remain unmeasured and unconstrained. Fast Radio Bursts (FRBs) offer an unparalleled method to measure the electron dispersion measures (DMs) of ionized baryons. Their distribution can resolve the missing baryon problem, and constrain the history of feedback theorized to impart significant energy to the CGM and IGM. We analyze the Cosmology and Astrophysics in Machine Learning (CAMEL) Simulations, using three suites: IllustrisTNG, SIMBA, and Astrid, each varying 6 parameters (2 cosmological & 4 astrophysical feedback), for a total of 183 distinct simulation models. We find significantly different predictions between the fiducial models of the suites, owing to their different implementations of feedback. SIMBA exhibits the strongest feedback, leading to the smoothest distribution of baryons, reducing the sightline-to-sightline variance in DMs between z=0-1. Astrid has the weakest feedback and the largest variance. We calculate FRB CGM measurements as a function of galaxy impact parameter, with SIMBA showing the weakest DMs due to aggressive AGN feedback and Astrid the strongest. Within each suite, the largest differences are due to varying AGN feedback. IllustrisTNG shows the most sensitivity to supernova feedback, but this is due to the change in the AGN feedback strengths, demonstrating that black holes, not stars, are most capable of redistributing baryons in the IGM and CGM. We compare our statistics directly to recent observations, paving the way for the use of FRBs to constrain the physics of galaxy formation and evolution.

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arXiv2024

The Impact of Non-Gaussian Primordial Tails on Cosmological Observables

Whilst current observational evidence favors a close-to-Gaussian spectrum of primordial perturbations, there exist many models of the early Universe that predict this distribution to have exponentially enhanced or suppressed tails. In this work, we generate realizations of the primordial potential with non-Gaussian tails via a phenomenological model; these are then evolved numerically to obtain maps of the cosmic microwave background (CMB) and large-scale structure (LSS). In the CMB maps, our added non-Gaussianity manifests as a localized enhancement of hot and cold spots, which would be expected to contribute to $N$-point functions up to large $N$. Such models are indirectly constrained by \textit{Planck} trispectrum bounds, which restrict the changes in the temperature fluctuations to $O(10μ\mathrm{K})$. In the late-time Universe, we find that tailed cosmologies lead to a halo mass function enhanced at high masses, as expected. Furthermore, significant scale-dependent bias in the halo-halo and halo-matter power spectrum is also sourced, which arises from the squeezed limit of large $N$-point functions that are implicitly generated through the enhancement of the tails. These results underscore that a detection of scale-dependent bias alone cannot be used to rule out single field inflation, but can be used together with other statistics to probe a wide range of primordial processes.

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arXiv2024

Denoising Diffusion Delensing Delight: Reconstructing the Non-Gaussian CMB Lensing Potential with Diffusion Models

Optimal extraction of cosmological information from observations of the Cosmic Microwave Background critically relies on our ability to accurately undo the distortions caused by weak gravitational lensing. In this work, we demonstrate the use of denoising diffusion models in performing Bayesian lensing reconstruction. We show that score-based generative models can produce accurate, uncorrelated samples from the CMB lensing convergence map posterior, given noisy CMB observations. To validate our approach, we compare the samples of our model to those obtained using established Hamiltonian Monte Carlo methods, which assume a Gaussian lensing potential. We then go beyond this assumption of Gaussianity, and train and validate our model on non-Gaussian lensing data, obtained by ray-tracing N-body simulations. We demonstrate that in this case, samples from our model have accurate non-Gaussian statistics beyond the power spectrum. The method provides an avenue towards more efficient and accurate lensing reconstruction, that does not rely on an approximate analytic description of the posterior probability. The reconstructed lensing maps can be used as an unbiased tracer of the matter distribution, and to improve delensing of the CMB, resulting in more precise cosmological parameter inference.

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arXiv2024

Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine learning and Simulations

We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over dark matter physics, astrophysics, and cosmology in modeling a range of systems -- from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of Warm Dark Matter, each comprised of 1024 simulations generated using the Arepo code. One suite consists of uniform-box simulations covering a $(25~h^{-1}~{\rm M}_\odot)^3$ volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the Warm Dark Matter particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and Convolutional Neural Networks, to demonstrate how such simulation suites can be used to disentangle the effects of dark matter and baryonic physics on galactic properties. The DREAMS project can be extended further to include different dark matter models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of dark matter on these scales.

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arXiv2024

Atomic Hydrogen Shows its True Colours: Correlations between HI and Galaxy Colour in Simulations

Intensity mapping experiments are beginning to measure the spatial distribution of neutral atomic hydrogen (HI) to constrain cosmological parameters and the large-scale distribution of matter. However, models of the behaviour of HI as a tracer of matter are complicated by galaxy evolution. In this work, we examine the clustering of HI in relation to galaxy colour, stellar mass, and HI mass in IllustrisTNG at $z$ = 0, 0.5, and 1. We compare the HI-red and HI-blue galaxy cross-power spectra, finding that HI-red has an amplitude 1.5 times higher than HI-blue at large scales. The cross-power spectra intersect at $\approx 3$ Mpc in real space and $\approx 10$ Mpc in redshift space, consistent with $z \approx 0$ observations. We show that HI clustering increases with galaxy HI mass and depends weakly on detection limits in the range $M_{\mathrm{HI}} \leq 10^8 M_\odot$. In terms of $M_\star$, we find blue galaxies in the greatest stellar mass bin cluster more than blue galaxies in other stellar mass bins. Red galaxies in the greatest stellar mass bin, however, cluster the weakest amongst red galaxies. These trends arise due to central-satellite compositions. Centrals correlate less with HI for increasing stellar mass, whereas satellites correlate more, irrespective of colour. Despite the clustering relationships with stellar mass, we find that the cross-power spectra are largely insensitive to detection limits in HI and galaxy surveys. Counter-intuitively, all auto and cross-power spectra for red and blue galaxies and HI decrease with time at all scales in IllustrisTNG. We demonstrate that processes associated with quenching contribute to this trend. The complex interplay between HI and galaxies underscores the importance of understanding baryonic effects when interpreting the large-scale clustering of HI, blue, and red galaxies at $z \leq 1$.

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arXiv2024

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $χ^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

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arXiv2024

Zooming by in the CARPoolGP lane: new CAMELS-TNG simulations of zoomed-in massive halos

Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic structures and galaxy populations. It is computationally challenging to sample these high dimensional parameter spaces with simulations, particularly for halos in the high-mass end of the mass function. In this work, we develop a novel sampling and reduced variance regression method, CARPoolGP, which leverages built-in correlations between samples in different locations of high dimensional parameter spaces to provide an efficient way to explore parameter space and generate low variance emulations of summary statistics. We use this method to extend the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) to include a set of 768 zoom-in simulations of halos in the mass range of $10^{13} - 10^{14.5} M_\odot\,h^{-1}$ that span a 28-dimensional parameter space in the IllustrisTNG model. With these simulations and the CARPoolGP emulation method, we explore parameter trends in the Compton $Y-M$, black hole mass-halo mass, and metallicity-mass relations, as well as thermodynamic profiles and quenched fractions of satellite galaxies. We use these emulations to provide a physical picture of the complex interplay between supernova and active galactic nuclei feedback. We then use emulations of the $Y-M$ relation of massive halos to perform Fisher forecasts on astrophysical parameters for future Sunyaev-Zeldovich observations and find a significant improvement in forecasted constraints. We publicly release both the simulation suite and CARPoolGP software package.

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arXiv2024

Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS

Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between dark matter density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in the galaxy formation models, that remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct dark matter fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and sub-grid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying dark matter fields from the given stellar mass fields, while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes approximately 500 times larger than those it was trained on, and across different galaxy formation models. Code for reproducing these results can be found at https://github.com/victoriaono/variational-diffusion-cdm

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arXiv2024

Quijote-PNG: Optimizing the summary statistics to measure Primordial non-Gaussianity

We apply a suite of different estimators to the Quijote-PNG halo catalogues to find the best approach to constrain Primordial non-Gaussianity (PNG) at non-linear cosmological scales, up to $k_{\rm max} = 0.5 \, h\,{\rm Mpc}^{-1}$. The set of summary statistics considered in our analysis includes the power spectrum, bispectrum, halo mass function, marked power spectrum, and marked modal bispectrum. Marked statistics are used here for the first time in the context of PNG study. We perform a Fisher analysis to estimate their cosmological information content, showing substantial improvements when marked observables are added to the analysis. Starting from these summaries, we train deep neural networks (NN) to perform likelihood-free inference of cosmological and PNG parameters. We assess the performance of different subsets of summary statistics; in the case of $f_\mathrm{NL}^\mathrm{equil}$, we find that a combination of the power spectrum and a suitable marked power spectrum outperforms the combination of power spectrum and bispectrum, the baseline statistics usually employed in PNG analysis. A minimal pipeline to analyse the statistics we identified can be implemented either with our ML algorithm or via more traditional estimators, if these are deemed more reliable.

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arXiv2024

Quijote-PNG: The Information Content of the Halo Mass Function

We study signatures of primordial non-Gaussianity (PNG) in the redshift-space halo field on non-linear scales, using a combination of three summary statistics, namely the halo mass function (HMF), power spectrum, and bispectrum. The choice of adding the HMF to our previous joint analysis of power spectrum and bispectrum is driven by a preliminary field-level analysis, in which we train graph neural networks on halo catalogues to infer the PNG $f_\mathrm{NL}$ parameter. The covariance matrix and the responses of our summaries to changes in model parameters are extracted from a suite of halo catalogues constructed from the Quijote-PNG N-body simulations. We consider the three main types of PNG: local, equilateral and orthogonal. Adding the HMF to our previous joint analysis of power spectrum and bispectrum produces two main effects. First, it reduces the equilateral $f_\mathrm{NL}$ predicted errors by roughly a factor $2$, while also producing notable, although smaller, improvements for orthogonal PNG. Second, it helps break the degeneracy between the local PNG amplitude, $f_\mathrm{NL}^\mathrm{local}$, and assembly bias, $b_ϕ$, without relying on any external prior assumption. Our final forecasts for PNG parameters are $Δf_\mathrm{NL}^\mathrm{local} = 40$, $Δf_\mathrm{NL}^\mathrm{equil} = 210$, $Δf_\mathrm{NL}^\mathrm{ortho} = 91$, on a cubic volume of $1 \left(h^{-1}{\rm Gpc}\right)^3$, with a halo number density of $\bar{n}\sim 5.1 \times 10^{-5}~h^3\mathrm{Mpc}^{-3}$, at $z = 1$, and considering scales up to $k_\mathrm{max} = 0.5~h\,\mathrm{Mpc}^{-1}$.

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arXiv2024

Taming assembly bias for primordial non-Gaussianity

Primordial non-Gaussianity of the local type induces a strong scale-dependent bias on the clustering of halos in the late-time Universe. This signature is particularly promising to provide constraints on the non-Gaussianity parameter $f_{\rm NL}$ from galaxy surveys, as the bias amplitude grows with scale and becomes important on large, linear scales. However, there is a well-known degeneracy between the real prize, the $f_{\rm NL}$ parameter, and the (non-Gaussian) assembly bias i.e., the halo formation history-dependent contribution to the amplitude of the signal, which could seriously compromise the ability of large-scale structure surveys to constrain $f_{\rm NL}$. We show how the assembly bias can be modeled and constrained, thus almost completely recovering the power of galaxy surveys to competitively constrain primordial non-Gaussianity. In particular, studying hydrodynamical simulations, we find that a proxy for the halo properties that determine assembly bias can be constructed from photometric properties of galaxies. Using a prior on the assembly bias guided by this proxy degrades the statistical errors on $f_{\rm NL}$ only mildly compared to an ideal case where the assembly bias is perfectly known. The systematic error on $f_{\rm NL}$ that the proxy induces can be safely kept under control.

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arXiv2024

Can we constrain warm dark matter masses with individual galaxies?

We study the impact of warm dark matter mass on the internal properties of individual galaxies using a large suite of 1,024 state-of-the-art cosmological hydrodynamic simulations from the DREAMS project. We take individual galaxies' properties from the simulations, which have different cosmologies, astrophysics, and warm dark matter masses, and train normalizing flows to learn the posterior of the parameters. We find that our models cannot infer the value of the warm dark matter mass, even when the values of the cosmological and astrophysical parameters are given explicitly. This result holds for galaxies with stellar mass larger than $2\times10^8 M_\odot/h$ at both low and high redshifts. We calculate the mutual information and find no significant dependence between the WDM mass and galaxy properties. On the other hand, our models can infer the value of $Ω_{\rm m}$ with a $\sim10\%$ accuracy from the properties of individual galaxies while marginalizing astrophysics and warm dark matter masses.

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arXiv2024

A field-level emulator for modeling baryonic effects across hydrodynamic simulations

We develop a new and simple method to model baryonic effects at the field level relevant for weak lensing analyses. We analyze thousands of state-of-the-art hydrodynamic simulations from the CAMELS project, each with different cosmology and strength of feedback, and we find that the cross-correlation coefficient between full hydrodynamic and N-body simulations is very close to 1 down to $k\sim10~h{\rm Mpc}^{-1}$. This suggests that modeling baryonic effects at the field level down to these scales only requires N-body simulations plus a correction to the mode's amplitude given by: $\sqrt{P_{\rm hydro}(k)/P_{\rm nbody}(k)}$. In this paper, we build an emulator for this quantity, using Gaussian processes, that is flexible enough to reproduce results from thousands of hydrodynamic simulations that have different cosmologies, astrophysics, subgrid physics, volumes, resolutions, and at different redshifts. Our emulator is accurate at the percent level and exhibits a range of validation superior to previous studies. This method and our emulator enable field-level simulation-based inference analyses and accounting for baryonic effects in weak lensing analyses.

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arXiv2024

An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks

The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near future, we develop a likelihood-free Deep Learning technique using convolutional neural networks (CNNs) to infer broad-scale physical properties of a galaxy's CGM and its halo mass for the first time. Using CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) data, including IllustrisTNG, SIMBA, and Astrid models, we train CNNs on Soft X-ray and 21-cm (HI) radio 2D maps to trace hot and cool gas, respectively, around galaxies, groups, and clusters. Our CNNs offer the unique ability to train and test on ''multifield'' datasets comprised of both HI and X-ray maps, providing complementary information about physical CGM properties and improved inferences. Applying eRASS:4 survey limits shows that X-ray is not powerful enough to infer individual halos with masses $\log(M_{\rm{halo}}/M_{\odot}) < 12.5$. The multifield improves the inference for all halo masses. Generally, the CNN trained and tested on Astrid (SIMBA) can most (least) accurately infer CGM properties. Cross-simulation analysis -- training on one galaxy formation model and testing on another -- highlights the challenges of developing CNNs trained on a single model to marginalize over astrophysical uncertainties and perform robust inferences on real data. The next crucial step in improving the resulting inferences on physical CGM properties hinges on our ability to interpret these deep-learning models.

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