arXiv subjects

Tiago Castro

Publications and source records attributed to Tiago Castro.

35 records · Page 2Linked to original sources

arXiv2023

Cosmological constraints from abundance, weak-lensing and clustering of galaxy clusters: application to the SDSS

The clustering of galaxy clusters is a powerful cosmological tool, which can help to break degeneracies between parameters when combined with other cosmological observables. We aim to demonstrate its potential in constraining cosmological parameters and scaling relations when combined with cluster counts and weak lensing mass information, using as a case study the redMaPPer cluster catalog derived from the Sloan Digital Sky Survey (SDSS). We extend the analysis of number counts and weak lensing signal performed by Costanzi et al. 2019a, with the addition of the real-space 2-point correlation function. We derive cosmological and scaling relation posteriors for all the possible combinations of the three observables to assess their constraining power, parameter degeneracies, and possible internal tensions. We find no evidence for tensions between the three data set analyzed. We demonstrate that the inclusion of the cluster clustering statistic can greatly enhance the constraining power of the sample thanks to its capability of breaking the $Ω_{\rm m} - σ_8$ degeneracy characteristic of cluster abundance studies. In particular, for a flat $Λ$CDM model with massive neutrinos, we obtain $Ω_{\rm m}=0.28 \pm 0.03$ and $σ_8 = 0.82 \pm 0.05$, a 33% and 50% improvement compared to the posteriors derived combining cluster abundance and weak lensing analyses. Our results are consistent with cosmological posteriors from other cluster surveys, as well as with Planck CMB results and DES-Y3 galaxy clustering and weak-lensing analysis.

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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

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

A universal equation to predict $Ω_{\rm m}$ from halo and galaxy catalogues

We discover analytic equations that can infer the value of $Ω_{\rm m}$ from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer $Ω_{\rm m}$ with $\sim6\%$ accuracy from halo catalogues of thousands of N-body simulations run with six different codes: Abacus, CUBEP$^3$M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict $Ω_{\rm m}$ from halo catalogues of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that by tuning a single free parameter, our equations can also infer the value of $Ω_{\rm m}$ from galaxy catalogues of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogues from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and $Ω_{\rm m}$, one that is not affected by galaxy formation physics down to scales as small as $10~h^{-1}{\rm kpc}$.

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arXiv2023

KiDS-1000 Cosmology: Constraints from density split statistics

Context. Weak lensing and clustering statistics beyond two-point functions can capture non-Gaussian information about the matter density field, thereby improving the constraints on cosmological parameters relative to the mainstream methods based on correlation functions and power spectra. Aims. This paper presents a cosmological analysis of the fourth data release of the Kilo Degree Survey based on the density split statistics, which measures the mean shear profiles around regions classified according to foreground densities. The latter is constructed from a bright galaxy sample, which we further split into red and blue samples, allowing us to probe their respective connection to the underlying dark matter density. Methods. We use the state-of-the-art model of the density splitting statistics and validate its robustness against mock data infused with known systematic effects such as intrinsic galaxy alignment and baryonic feedback. Results. After marginalising over the photometric redshift uncertainty and the residual shear calibration bias, we measure for the full KiDS-bright sample a structure growth parameter of $S_8 = σ_8 \sqrt{Ω_\mathrm{m}/0.3} = 0.74^{+0.03}_{-0.02}$ that is competitive to and consistent with two-point cosmic shear results, a matter density of $Ω_\mathrm{m} = 0.28 \pm 0.02$, and a constant galaxy bias of $b = 1.32^{+0.12}_{-0.10}$.

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arXiv2022

The lure of sirens: joint distance and velocity measurements with third generation detectors

The next generation of detectors will detect gravitational waves from binary neutron stars at cosmological distances, for which around a thousand electromagnetic follow-ups may be observed per year. So far, most work devoted to the expected cosmological impact of these standard sirens employed them only as distance indicators. Only recently their use as tracers of clustering, similar to what already proposed for supernovae, has been studied. Focusing on the expected specifications of the Einstein Telescope (ET), we forecast here the performance on cosmological parameters of future standard sirens as both distance and density indicators, with emphasis on the linear perturbation growth index and on spatial curvature. We improve upon previous studies in a number of ways: a more detailed analysis of available telescope time, the inclusion of more cosmological and nuisance parameters, the Alcock-Paczynski correction, the use of sirens also as both velocity and density tracers, and a more accurate estimation of the distance posterior. We find that the analysis of the clustering of sirens improves the constraints on $H_0$ by 30% and on $Ω_{k0}$ by over an order of magnitude, with respect to their use merely as distance indicators. With 5 years of joint ET and Rubin Observatory follow-ups we could reach precision of 0.1 km/s/Mpc in $H_0$ and 0.02 in $Ω_{k0}$ using only data in the range $0<z<0.5$. We also find that the use of sirens as tracers of density, and not only velocity, yields good improvements on the growth of structure constraints.

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arXiv2022

Satellite galaxy abundance dependency on cosmology in Magneticum simulations

Context: Modelling satellite galaxy abundance $N_s$ in Galaxy Clusters (GCs) is a key element in modelling the Halo Occupation Distribution (HOD), which itself is a powerful tool to connect observational studies with numerical simulations. Aims: To study the impact of cosmological parameters on satellite abundance both in cosmological simulations and in mock observations. Methods: We build an emulator (HODEmu, \url{https://github.com/aragagnin/HODEmu/}) of satellite abundance based on cosmological parameters $Ω_m, Ω_b, σ_8, h_0$ and redshift $z.$ We train our emulator using \magneticum hydrodynamic simulations that span 15 different cosmologies, each over $4$ redshift slices between $0<z<0.5,$ and for each setup we fit normalisation $A$, log-slope $β$ and Gaussian fractional-scatter $σ$ of the $N_s-M$ relation. The emulator is based on multi-variate output Gaussian Process Regression (GPR). Results: We find that $A$ and $β$ depend on cosmological parameters, even if weakly, especially on $Ω_m,$ $Ω_b.$ This dependency can explain some discrepancies found in literature between satellite HOD of different cosmological simulations (Magneticum, Illustris, BAHAMAS). We also show that satellite abundance cosmology dependency differs between full-physics (FP) simulations, dark-matter only (DMO), and non-radiative simulations. Conclusions: This work provides a preliminary calibration of the cosmological dependency of the satellite abundance of high mass halos, and we showed that modelling HOD with cosmological parameters is necessary to interpret satellite abundance, and we showed the importance of using FP simulations in modelling this dependency.

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arXiv2022

Robust field-level inference with dark matter halos

We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $Ω_{\rm m}$ and $σ_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $Ω_{\rm m}$ and $σ_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $Ω_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters.

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arXiv2022

Persistent homology in cosmic shear II: A tomographic analysis of DES-Y1

We demonstrate how to use persistent homology for cosmological parameter inference in a tomographic cosmic shear survey. We obtain the first cosmological parameter constraints from persistent homology by applying our method to the first-year data of the Dark Energy Survey. To obtain these constraints, we analyse the topological structure of the matter distribution by extracting persistence diagrams from signal-to-noise maps of aperture masses. This presents a natural extension to the widely used peak count statistics. Extracting the persistence diagrams from the cosmo-SLICS, a suite of $N$-body simulations with variable cosmological parameters, we interpolate the signal using Gaussian Processes and marginalise over the most relevant systematic effects, including intrinsic alignments and baryonic effects. We find for the structure growth parameter $S_8=0.747^{+0.025}_{-0.031}$, which is in full agreement with other late-time probes. We also constrain the intrinsic alignment parameter to $A=1.54\pm 0.52$, ruling out the case of no intrinsic alignments at a $3σ$-level.

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arXiv2021

Cosmic Shear Cosmology Beyond 2-Point Statistics: A Combined Peak Count and Correlation Function Analysis of DES-Y1

We constrain cosmological parameters from a joint cosmic shear analysis of peak-counts and the two-point shear correlation functions, as measured from the Dark Energy Survey (DES-Y1). We find the structure growth parameter $S_8\equiv σ_8\sqrt{Ω_{\rm m}/0.3} = 0.766^{+0.033}_{-0.038}$, which at 4.8% precision, provides one of the tightest constraints on $S_8$ from the DES-Y1 weak lensing data. In our simulation-based method we determine the expected DES-Y1 peak-count signal for a range of cosmologies sampled in four $w$CDM parameters ($Ω_{\rm m}$, $σ_8$, $h$, $w_0$). We also determine the joint covariance matrix with over 1000 realisations at our fiducial cosmology. With mock DES-Y1 data we calibrate the impact of photometric redshift and shear calibration uncertainty on the peak-count, marginalising over these uncertainties in our cosmological analysis. Using dedicated training samples we show that our measurements are unaffected by mass resolution limits in the simulation, and that our constraints are robust against uncertainty in the effect of baryon feedback. Accurate modelling for the impact of intrinsic alignments on the tomographic peak-count remains a challenge, currently limiting our exploitation of cross-correlated peak counts between high and low redshift bins. We demonstrate that once calibrated, a fully tomographic joint peak-count and correlation functions analysis has the potential to reach a 3% precision on $S_8$ for DES-Y1. Our methodology can be adopted to model any statistic that is sensitive to the non-Gaussian information encoded in the shear field. In order to accelerate the development of these beyond-two-point cosmic shear studies, our simulations are made available to the community upon request.

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arXiv2021

Impact of baryons in cosmic shear analyses with tomographic aperture mass statistics

NonGaussian cosmic shear statistics based on weak-lensing aperture mass ($M_{\rm ap}$) maps can outperform the classical shear two-point correlation function ($γ$-2PCF) in terms of cosmological constraining power. However, reaching the full potential of these new estimators requires accurate modeling of the physics of baryons as the extra nonGaussian information mostly resides at small scales. We present one such modeling based on the Magneticum hydrodynamical simulation for the KiDS-450 and DES-Y1 surveys and a Euclid-like survey. We compute the bias due to baryons on the lensing PDF and the distribution of peaks and voids in $M_{\rm ap}$ maps and propagate it to the cosmological forecasts on the structure growth parameter $S_8$, the matter density parameter $Ω_{\rm m}$, and the dark energy equation of state $w_0$ using the SLICS and cosmo-SLICS sets of dark-matter-only simulations. We report a negative bias of a few percent on $S_8$ and $Ω_{\rm m}$ and also measure a positive bias of the same level on $w_0$ when including a tomographic decomposition. These biases reach $\sim 5$% when combining $M_{\rm ap}$ statistics with the $γ$-2PCF as these estimators show similar dependency on the AGN feedback. We verify that these biases constitute a less than $1σ$ shift on the probed cosmological parameters for current cosmic shear surveys. However, baryons need to be accounted for at the percentage level for future Stage IV surveys and we propose to include the uncertainty on the AGN feedback amplitude by marginalizing over this parameter using multiple simulations such as those presented in this paper. Finally, we explore the possibility of mitigating the impact of baryons by filtering the $M_{\rm ap}$ map but find that this process would require to suppress the small-scale information to a point where the constraints would no longer be competitive.

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arXiv2020

Testing the Reliability of Fast Methods for Weak Lensing Simulations: WL-MOKA on PINOCCHIO

The generation of simulated convergence maps is of key importance in fully exploiting weak lensing by Large Scale Structure (LSS) from which cosmological parameters can be derived. In this paper we present an extension of the PINOCCHIO code which produces catalogues of dark matter haloes so that it is capable of simulating weak lensing by LSS. Like WL-MOKA, the method starts with a random realisation of cosmological initial conditions, creates a halo catalogue and projects it onto the past-light-cone, and paints in haloes assuming parametric models for the mass density distribution within them. Large scale modes that are not accounted for by the haloes are constructed using linear theory. We discuss the systematic errors affecting the convergence power spectra when Lagrangian Perturbation Theory at increasing order is used to displace the haloes within PINOCCHIO, and how they depend on the grid resolution. Our approximate method is shown to be very fast when compared to full ray-tracing simulations from an N-Body run and able to recover the weak lensing signal, at different redshifts, with a few percent accuracy. It also allows for quickly constructing weak lensing covariance matrices, complementing PINOCCHIO's ability of generating the cluster mass function and galaxy clustering covariances and thus paving the way for calculating cross covariances between the different probes. This work advances these approximate methods as tools for simulating and analysing surveys data for cosmological purposes.

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arXiv2018

The effect of baryons in the cosmological lensing PDFs

Observational cosmology is passing through a unique moment of grandeur with the amount of quality data growing fast. However, in order to better take advantage of this moment, data analysis tools have to keep up the pace. Understanding the effect of baryonic matter on the large-scale structure is one of the challenges to be faced in cosmology. In this work, we have thoroughly studied the effect of baryonic physics on different lensing statistics. Making use of the Magneticum Pathfinder suite of simulations we show that the influence of luminous matter on the 1-point lensing statistics of point sources is significant, enhancing the probability of magnified objects with $μ>3$ by a factor of $2$ and the occurrence of multiple-images by a factor $5-500$ depending on the source redshift and size. We also discuss the dependence of the lensing statistics on the angular resolution of sources. Our results and methodology were carefully tested in order to guarantee that our uncertainties are much smaller than the effects here presented.

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arXiv2016

Constraining the halo mass function with observations

The abundances of dark matter halos in the universe are described by the halo mass function (HMF). It enters most cosmological analyses and parametrizes how the linear growth of primordial perturbations is connected to these abundances. Interestingly, this connection can be made approximately cosmology independent. This made it possible to map in detail its near-universal behavior through large-scale simulations. However, such simulations may suffer from systematic effects, especially if baryonic physics is included. In this paper we ask how well observations can constrain directly the HMF. The observables we consider are galaxy cluster number counts, galaxy cluster power spectrum and lensing of type Ia supernovae. Our results show that DES is capable of putting the first meaningful constraints on the HMF, while both Euclid and J-PAS can give stronger constraints, comparable to the ones from state-of-the-art simulations. We also find that an independent measurement of cluster masses is even more important for measuring the HMF than for constraining the cosmological parameters, and can vastly improve the determination of the halo mass function. Measuring the HMF could thus be used to cross-check simulations and their implementation of baryon physics. It could even, if deviations cannot be accounted for, hint at new physics.

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arXiv2016

Turning noise into signal: learning from the scatter in the Hubble diagram

The supernova (SN) Hubble diagram residual contains valuable information on both the present matter power spectrum and its growth history. In this paper we show that this information can be retrieved with precision by combining both peculiar velocity and weak-lensing analysis on the data. To wit, peculiar velocity induces correlations on the nearby SN while lensing induces a non-Gaussian dispersion in faraway objects. We show that both effects have almost orthogonal degeneracies and discuss how they can be extracted simultaneously from the data. We analyze the JLA supernova catalog in a 14-dimensional parameter space, assuming a flexible growth-rate index $γ$. We arrive at the following marginalized constraints: $σ_8 = 0.65^{+0.23}_{-0.37}$ and $γ= 1.38^{+1.7}_{-0.65}$. Assuming instead GR as the correct gravitation theory (and thus $γ\equiv 0.55$), the constraints in $σ_8$ tighten further: $σ_8 = 0.40^{+0.21}_{-0.23}$. We show that these constraints complement well the ones obtained from other datasets and that they could improve substantially with more SNe.

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arXiv2015

Constraining the growth of perturbations with lensing of supernovae

A recently proposed technique allows one to constrain both the background and perturbation cosmological parameters through the distribution function of supernova Ia apparent magnitudes. Here we extend this technique to alternative cosmological scenarios, in which the growth of structure does not follow the $Λ$CDM prescription. We apply the method first to the supernova data provided by the JLA catalog combined with all the current independent redshift distortion data and with low-redshift cluster data from Chandra and show that although the supernovae alone are not very constraining, they help in reducing the confidence regions. Then we apply our method to future data from LSST and from a survey that approximates the Euclid satellite mission. In this case we show that the combined data are nicely complementary and can constrain the normalization $σ_8$ and the growth rate index $γ$ to within $0.6\%$ and $7\%$, respectively. In particular, the LSST supernova catalog is forecast to give the constraint $γ(σ_8/0.83)^{6.7} = 0.55 \pm 0.1$. We also report on constraints relative to a step-wise parametrization of the growth rate of structures. These results show that supernova lensing serves as a good cross-check on the measurement of perturbation parameters from more standard techniques.

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arXiv2014

First measurement of $σ_8$ using supernova magnitudes only

A method was recently proposed which allows the conversion of the weak-lensing effects in the supernova Hubble diagram from noise into signal. Such signal is sensitive to the growth of structure in the universe, and in particular can be used as a measurement of $σ_8$ which is independent from more traditional methods such as those based on the CMB, cosmic shear or cluster abundance. We extend here that analysis to allow for intrinsic non-Gaussianities in the supernova PDF, and discuss how this can be best modelled using the Bayes Factor. Although it was shown that a precise measurement of $σ_8$ requires ~$10^5$ supernovae, current data already allows an important proof of principle. In particular we make use of the 732 supernovae with z < 1 of the recent JLA catalog and show that a simple treatment of intrinsic non-Gaussianities with a couple of nuisance parameters is enough for our method to yield the values $σ_8 = 0.84^{+0.28}_{-0.65}$ or $σ_8 < 1.45$ at a $2σ$ confidence level. This result is consistent with mock simulations and it is also in agreement with independent measurements and presents the first ever measurement of $σ_8$ using supernova magnitudes alone.

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