arXiv subjects

Alexandre Refregier

Publications and source records attributed to Alexandre Refregier.

At least 37 records · Page 2Linked to original sources

arXiv2024

Simulation-based inference of deep fields: galaxy population model and redshift distributions

Accurate redshift calibration is required to obtain unbiased cosmological information from large-scale galaxy surveys. In a forward modelling approach, the redshift distribution n(z) of a galaxy sample is measured using a parametric galaxy population model constrained by observations. We use a model that captures the redshift evolution of the galaxy luminosity functions, colours, and morphology, for red and blue samples. We constrain this model via simulation-based inference, using factorized Approximate Bayesian Computation (ABC) at the image level. We apply this framework to HSC deep field images, complemented with photometric redshifts from COSMOS2020. The simulated telescope images include realistic observational and instrumental effects. By applying the same processing and selection to real data and simulations, we obtain a sample of n(z) distributions from the ABC posterior. The photometric properties of the simulated galaxies are in good agreement with those from the real data, including magnitude, colour and redshift joint distributions. We compare the posterior n(z) from our simulations to the COSMOS2020 redshift distributions obtained via template fitting photometric data spanning the wavelength range from UV to IR. We mitigate sample variance in COSMOS by applying a reweighting technique. We thus obtain a good agreement between the simulated and observed redshift distributions, with a difference in the mean at the 1$σ$ level up to a magnitude of 24 in the i band. We discuss how our forward model can be applied to current and future surveys and be further extended. The ABC posterior and further material will be made publicly available at https://cosmology.ethz.ch/research/software-lab/ufig.html.

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arXiv2024

Fast Forward Modelling of Galaxy Spatial and Statistical Distributions

A forward modelling approach provides simple, fast and realistic simulations of galaxy surveys, without a complex underlying model. For this purpose, galaxy clustering needs to be simulated accurately, both for the usage of clustering as its own probe and to control systematics. We present a forward model to simulate galaxy surveys, where we extend the Ultra-Fast Image Generator to include galaxy clustering. We use the distribution functions of the galaxy properties, derived from a forward model adjusted to observations. This population model jointly describes the luminosity functions, sizes, ellipticities, SEDs and apparent magnitudes. To simulate the positions of galaxies, we then use a two-parameter relation between galaxies and halos with Subhalo Abundance Matching (SHAM). We simulate the halos and subhalos using the fast PINOCCHIO code, and a method to extract the surviving subhalos from the merger history. Our simulations contain a red and a blue galaxy population, for which we build a SHAM model based on star formation quenching. For central galaxies, mass quenching is controlled with the parameter M$_{\mathrm{limit}}$, with blue galaxies residing in smaller halos. For satellite galaxies, environmental quenching is implemented with the parameter t$_{\mathrm{quench}}$, where blue galaxies occupy only recently merged subhalos. We build and test our model by comparing to imaging data from the Dark Energy Survey Year 1. To ensure completeness in our simulations, we consider the brightest galaxies with $i<20$. We find statistical agreement between our simulations and the data for two-point correlation functions on medium to large scales. Our model provides constraints on the two SHAM parameters M$_{\mathrm{limit}}$ and t$_{\mathrm{quench}}$ and offers great prospects for the quick generation of galaxy mock catalogues, optimized to agree with observations.

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arXiv2024

N-Body Simulation of Early Structure Formation from Cosmic String Loops

By means of N-body simulations, we study early structure formation in the presence of a scaling distribution of cosmic string loops. Cosmic string loops dominate the high redshift halo mass function while the fluctuations seeded by the standard structure formation scenario dominate structure at low redshifts. In our study, the effects of the cosmic string loops are taken into account by displacing the dark matter particles and their velocities at the initial time of the simulation by amounts determined by the analytical analysis which makes use of the Zeldovich approximation. We find that the resulting halo mass function is to a good approximation given by the sum of the analytically determined cosmic string halo mass function and the halo mass function obtained from the standard $Λ$CDM model.

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arXiv2023

$\mathbf{12\times2}$pt combined probes: pipeline, neutrino mass, and data compression

With the rapid advance of wide-field surveys it is increasingly important to perform combined cosmological probe analyses. We present a new pipeline for simulation-based multi-probe analyses, which combines tomographic large-scale structure (LSS) probes (weak lensing and galaxy clustering) with cosmic microwave background (CMB) primary and lensing data. These are combined at the $C_\ell$-level, yielding 12 distinct auto- and cross-correlations. The pipeline is based on $\texttt{UFalconv2}$, a framework to generate fast, self-consistent map-level realizations of cosmological probes from input lightcones, which is applied to the $\texttt{CosmoGridV1}$ N-body simulation suite. It includes a non-Gaussian simulation-based covariance for the LSS tracers, several data compression schemes, and a neural network emulator for accelerated theoretical predictions. We validate our framework, apply it to a simulated $12\times2$pt tomographic analysis of KiDS, BOSS, and $\textit{Planck}$, and forecast constraints for a $Λ$CDM model with a variable neutrino mass. We find that, while the neutrino mass constraints are driven by the CMB data, the addition of LSS data helps to break degeneracies and improves the constraint by up to 35%. For a fiducial $M_ν=0.15\mathrm{eV}$, a full combination of the above CMB+LSS data would enable a $3σ$ constraint on the neutrino mass. We explore data compression schemes and find that MOPED outperforms PCA. We also study the impact of an internal lensing tension in the CMB data, parametrized by $A_L$, on the neutrino mass constraint, finding that the addition of LSS to CMB data including all cross-correlations is able to mitigate the impact of this systematic. $\texttt{UFalconv2}$ and a MOPED compressed $\textit{Planck}$ CMB primary + CMB lensing likelihood are made publicly available. [abridged]

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arXiv2023

Towards a full $w$CDM map-based analysis for weak lensing surveys

The next generation of weak lensing surveys will measure the matter distribution of the local Universe with unprecedented precision, allowing the resolution of non-Gaussian features of the convergence field. This encourages the use of higher-order mass-map statistics for cosmological parameter inference. We extend the forward-modelling based methodology introduced in a previous forecast paper to match these new requirements. We provide multiple forecasts for the wCDM parameter constraints that can be expected from stage 3 and 4 weak lensing surveys. We consider different survey setups, summary statistics and mass map filters including wavelets. We take into account the shear bias, photometric redshift uncertainties and intrinsic alignment. The impact of baryons is investigated and the necessary scale cuts are applied. We compare the angular power spectrum analysis to peak and minima counts as well as Minkowski functionals of the mass maps. We find a preference for Starlet over Gaussian filters. Our results suggest that using a survey setup with 10 instead of 5 tomographic redshift bins is beneficial. Adding cross-tomographic information improves the constraints on cosmology and especially on galaxy intrinsic alignment for all statistics. In terms of constraining power, we find the angular power spectrum and the peak counts to be equally matched for stage 4 surveys, followed by minima counts and the Minkowski functionals. Combining different summary statistics significantly improves the constraints and compensates the stringent scale cuts. We identify the most `cost-effective' combination to be the angular power spectrum, peak counts and Minkowski functionals following Starlet filtering.

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arXiv2023

Early Structure Formation from Cosmic String Loops in Light of Early JWST Observations

Cosmic strings, if they exist, source nonlinear and non-Gaussian perturbations all the way back to the time of equal matter and radiation (and earlier). Here, we compute the mass function of halos seeded by a scaling distribution of cosmic string loops, and we compare the results with the predictions of the standard Gaussian $Λ$CDM model. Assuming a simple linear relation between stellar mass and halo mass, we also compute the stellar mass function. The contribution of cosmic strings dominates at sufficiently high redshifts $z > z_c$ where $z_c$ depends on the mass of the halo and on the mass per unit length $μ$ of the strings and is of the order $z_c \sim 12$ for $Gμ= 10^{-8}$. We find that strings with this value of $Gμ$ can explain the preliminary JWST data on the high redshift stellar mass density. Based on an extreme value statistic, we find that the mass of the heaviest expected string-seeded galaxy for the current JWST sky coverage is compatible with the heaviest detected galaxy. Given the uncertainties in the interpretation of the JWST data, we discuss predictions for higher redshift observations.

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arXiv2022

Cosmology from Galaxy Redshift Surveys with PointNet

In recent years, deep learning approaches have achieved state-of-the-art results in the analysis of point cloud data. In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. These surveys have so far mostly been analysed with two-point statistics, such as power spectra and correlation functions. The usage of these summary statistics is best justified on large scales, where the density field is linear and Gaussian. However, in light of the increased precision expected from upcoming surveys, the analysis of -- intrinsically non-Gaussian -- small angular separations represents an appealing avenue to better constrain cosmological parameters. In this work, we aim to improve upon two-point statistics by employing a \textit{PointNet}-like neural network to regress the values of the cosmological parameters directly from point cloud data. Our implementation of PointNets can analyse inputs of $\mathcal{O}(10^4) - \mathcal{O}(10^5)$ galaxies at a time, which improves upon earlier work for this application by roughly two orders of magnitude. Additionally, we demonstrate the ability to analyse galaxy redshift survey data on the lightcone, as opposed to previously static simulation boxes at a given fixed redshift.

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arXiv2022

Redshift requirements for cosmic shear with intrinsic alignment

Intrinsic alignment (IA) modelling and photometric redshift estimation are two of the main sources of systematic uncertainty in weak lensing surveys. We investigate the impact of redshift errors and their interplay with different IA models. Generally, errors on the mean $δ_z$ and on the width $σ_z$ of the redshift bins can both lead to biases in cosmological constraints. We find that such biases can, however, only be partially resolved by marginalizing over $δ_z$ and $σ_z$. For Stage-III surveys, $δ_z$ and $σ_z$ cannot be well constrained due to limited statistics. The resulting biases are thus sensitive to prior volume effects. For Stage-IV surveys, we observe that marginalizing over the redshift parameters has an impact and reduces the bias. We derive requirements on the uncertainty of $σ_z$ and $δ_z$ for both Stage-III and Stage-IV surveys. We assume that the redshift systematic errors on $S_8$ should be less than half of the statistical errors, and the median bias should be smaller than $0.25σ$. We find that the uncertainty on $δ_z$ has to be $\lesssim0.025$ for the NLA IA model with a Stage-III survey. For $σ_z$, the requirement is met even for large uncertainties $\leq0.3$. For the TATT IA model, the uncertainty on $δ_z$ has to be $\lesssim0.02$ and the uncertainty on $σ_z$ has to be $\lesssim0.2$. For Stage-IV surveys, the uncertainty on $δ_z$ has to be $\lesssim0.005$ and the uncertainty on $σ_z$ should be $\lesssim0.1$, with no significant dependence on the IA model. This required high precision will be a challenge for the redshift calibration of these future surveys. Finally, we investigate whether the interplay between redshift systematics and IA modelling can explain the $S_8$-tension between cosmic shear results and CMB measurements. We find that this is unlikely to explain the current $S_8$-tension.

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arXiv2022

CosmoGridV1: a simulated $w$CDM theory prediction for map-level cosmological inference

We present CosmoGridV1: a large set of lightcone simulations for map-level cosmological inference with probes of large scale structure. It is designed for cosmological parameter measurement based on Stage-III photometric surveys with non-Gaussian statistics and machine learning. CosmoGridV1 spans the $w$CDM model by varying $Ω_m$, $σ_8$, $w_0$, $H_0$, $n_s$, $Ω_b$, and assumes three degenerate neutrinos with $\sum m_ν$ = 0.06 eV. This space is covered by 2500 grid points on a Sobol sequence. At each grid point, we run 7 simulations with PkdGrav3 and store 69 particle maps at nside=2048 up to $z$=3.5, as well as halo catalog snapshots. The fiducial cosmology has 200 independent simulations, along with their stencil derivatives. An important part of CosmoGridV1 is the benchmark set of 28 simulations, which include larger boxes, higher particle counts, and higher redshift resolution of shells. They allow for testing if new types of analyses are sensitive to choices made in CosmoGridV1. We add baryon feedback effects on the map level, using shell-based baryon correction model. The shells are used to create maps of weak gravitational lensing, intrinsic alignment, and galaxy clustering, using the UFalcon code. The main part of CosmoGridV1 are the raw particle count shells that can be used to create full-sky maps for a given $n(z)$. We also release projected maps for a Stage-III forecast, as well as maps used previously in KiDS-1000 deep learning constraints with CosmoGridV1. The data is available at www.cosmogrid.ai.

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arXiv2022

Rapid Simulations of Halo and Subhalo Clustering

The analysis of cosmological galaxy surveys requires realistic simulations for their interpretation. Forward modelling is a powerful method to simulate galaxy clustering without the need for an underlying complex model. This approach requires fast cosmological simulations with a high resolution and large volume, to resolve small dark matter halos associated to single galaxies. In this work, we present fast halo and subhalo clustering simulations based on the Lagrangian perturbation theory code PINOCCHIO, which generates halos and merger trees. The subhalo progenitors are extracted from the merger history and the survival of subhalos is modelled. We introduce a new fitting function for the subhalo merger time, which includes a redshift dependence of the fitting parameters. The spatial distribution of subhalos within their hosts is modelled using a number density profile. We compare our simulations with the halo finder ROCKSTAR applied to the full N-body code GADGET-2. The subhalo velocity function and the correlation function of halos and subhalos are in good agreement. We investigate the effect of the chosen number density profile on the resulting subhalo clustering. Our simulation is approximate yet realistic and significantly faster compared to a full N-body simulation combined with a halo finder. The fast halo and subhalo clustering simulations offer good prospects for galaxy forward models using subhalo abundance matching.

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arXiv2022

Antenna characterization for the HIRAX experiment

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) aims to improve constraints on the dark energy equation of state through measurements of large-scale structure at high redshift ($0.8 $700MHz. Noise temperature measurements of the HIRAX feeds were performed in a custom apparatus built at Yale. In this system, identical loads, one cryogenic and the other at room temperature, are used to take a differential (Y-factor) measurement from which the noise of the system is inferred. Several measurement sets have been conducted using the system, involving CHIME feeds as well as four of the HIRAX active feeds. These measurements give the first noise temperature measurements of the HIRAX feed, revealing a $\sim$60K noise temperature (relative to 30K target) with 40K peak- to-peak frequency-dependent features, and provide the first demonstration of feed repeatability. Both findings inform current and future feed designs.

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arXiv2022

Assessing theoretical uncertainties for cosmological constraints from weak lensing surveys

$ $Weak gravitational lensing is a powerful probe which is used to constrain the standard cosmological model and its extensions. With the enhanced statistical precision of current and upcoming surveys, high accuracy predictions for weak lensing statistics are needed to limit the impact of theoretical uncertainties on cosmological parameter constraints. For this purpose, we present a comparison of the theoretical predictions for the nonlinear matter and weak lensing power spectra, based on the widely used fitting functions ($\texttt{mead}$ and $\texttt{rev-halofit}$), emulators ($\texttt{EuclidEmulator}$, $\texttt{EuclidEmulator2}$, $\texttt{BaccoEmulator}$ and $\texttt{CosmicEmulator}$) and N-body simulations ($\texttt{Pkdgrav3}$). We consider the forecasted constraints on the $Λ\texttt{CDM}$ and $\texttt{wCDM}$ models from weak lensing for stage III and stage IV surveys. We study the relative bias on the constraints and their dependence on the assumed prescriptions. Assuming a $Λ\texttt{CDM}$ cosmology, we find that the relative agreement on the $S_8$ parameter is between $0.2-0.3σ$ for a stage III-like survey between the above predictors. For a stage IV-like survey the agreement becomes $1.4-3.0σ$. In the $\texttt{wCDM}$ scenario, we find broader $S_8$ constraints, and agreements of $0.18-0.26σ$ and $0.7-1.7σ$ for stage III and stage IV surveys, respectively. The accuracies of the above predictors therefore appear adequate for stage III surveys, while the fitting functions would need improvements for future stage IV weak lensing surveys. Furthermore, we find that, of the fitting functions, $\texttt{mead}$ provides the best agreement with the emulators. We discuss the implication of these findings for the preparation of the future weak lensing surveys.

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arXiv2022

Symbolic Implementation of Extensions of the $\texttt{PyCosmo}$ Boltzmann Solver

$\texttt{PyCosmo}$ is a Python-based framework for the fast computation of cosmological model predictions. One of its core features is the symbolic representation of the Einstein-Boltzmann system of equations. Efficient $\texttt{C/C++}$ code is generated from the $\texttt{SymPy}$ symbolic expressions making use of the $\texttt{sympy2c}$ package. This enables easy extensions of the equation system for the implementation of new cosmological models. We illustrate this with three extensions of the $\texttt{PyCosmo}$ Boltzmann solver to include a dark energy component with a constant equation of state, massive neutrinos and a radiation streaming approximation. We describe the $\texttt{PyCosmo}$ framework, highlighting new features, and the symbolic implementation of the new models. We compare the $\texttt{PyCosmo}$ predictions for the $Λ$CDM model extensions with $\texttt{CLASS}$, both in terms of accuracy and computational speed. We find a good agreement, to better than 0.1% when using high-precision settings and a comparable computational speed. Links to the Python Package Index (PyPI) page of the code release and to the PyCosmo Hub, an online platform where the package is installed, are available at: https://cosmology.ethz.ch/research/software-lab/PyCosmo.html.

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arXiv2022

Constraining baryonic feedback and cosmology with weak-lensing, X-ray, and kinematic Sunyaev-Zeldovich observations

Modern weak-lensing observations are becoming increasingly sensitive to baryonic feedback processes which are still poorly understood. So far, this challenge has been faced either by imposing scale-cuts in the data or by modelling baryonic effects with simple, one-parameter models. In this paper, we rely on a more general, seven-parameter prescription of baryonic feedback effects, which is primarily motivated by observations and has been shown to agree with a plethora of hydrodynamical simulations. By combining weak-lensing data from the Kilo-Degree Survey (KiDS-1000) with observations of gas around galaxy clusters, we are able to constrain baryonic parameters and learn more about feedback and cosmology. In particular, we use cluster gas fractions from X-ray data and gas profiles from kinematic Sunyaev-Zeldovich (kSZ) observations to provide evidence for baryonic feedback that is stronger than predicted by most hydrodynamical simulations. In terms of the matter power spectrum, we report a beyond-percent effect at wave-modes above $k\sim 0.1-0.45$ h/Mpc and a maximum suppression of $12-33$ percent at $k\sim7$ h/Mpc (68 percent confidence level). Regarding the combined parameter $Σ_8=σ_8(Ω_m/0.3)^{0.58}$, we find the known tension with the Planck satellite data to be reduced from 3.8 to 2.9 $σ$ once baryonic effects are fully included in the analysis pipeline. The tension is further decreased to 2.6 $σ$ when the weak-lensing data is combined with X-ray and kSZ observations. We conclude that, while baryonic feedback effects become more important in modern weak-lensing surveys, they are unlikely to act as the main culprit for the observed $Σ_8$-tension.

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arXiv2022

Super-resolving Dark Matter Halos using Generative Deep Learning

Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology. In this work we predict high resolution dark matter halos from large scale, low resolution dark matter only simulations. This is achieved by mapping lower resolution to higher resolution density fields of simulations sharing the same cosmology, initial conditions and box-sizes. To resolve structure down to a factor of 8 increase in mass resolution, we use a variation of U-Net with a conditional GAN, generating output that visually and statistically matches the high resolution target extremely well. This suggests that our method can be used to create high resolution density output over Gpc/h box-sizes from low resolution simulations with negligible computational effort.

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arXiv2022

A Full $w$CDM Analysis of KiDS-1000 Weak Lensing Maps using Deep Learning

We present a full forward-modeled $w$CDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the $\texttt{CosmoGrid}$, a novel massive simulation suite spanning six different cosmological parameters, we generate almost one million tomographic mock surveys on the sphere. Due to the large data set size and survey area, we perform a spherical analysis while limiting our map resolution to $\texttt{HEALPix}$ $n_\mathrm{side}=512$. We marginalize over systematics such as photometric redshift errors, multiplicative calibration and additive shear bias. Furthermore, we use a map-level implementation of the non-linear intrinsic alignment model along with a novel treatment of baryonic feedback to incorporate additional astrophysical nuisance parameters. We also perform a spherical power spectrum analysis for comparison. The constraints of the cosmological parameters are generated using a likelihood free inference method called Gaussian Process Approximate Bayesian Computation (GPABC). Finally, we check that our pipeline is robust against choices of the simulation parameters. We find constraints on the degeneracy parameter of $S_8 \equiv σ_8\sqrt{Ω_M/0.3} = 0.78^{+0.06}_{-0.06}$ for our power spectrum analysis and $S_8 = 0.79^{+0.05}_{-0.05}$ for our GCNN analysis, improving the former by 16%. This is consistent with earlier analyses of the 2-point function, albeit slightly higher. Baryonic corrections generally broaden the constraints on the degeneracy parameter by about 10%. These results offer great prospects for full machine learning based analyses of on-going and future weak lensing surveys.

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arXiv2022

sympy2c: from symbolic expressions to fast C/C++ functions and ODE solvers in Python

Computer algebra systems play an important role in science as they facilitate the development of new theoretical models. The resulting symbolic equations are often implemented in a compiled programming language in order to provide fast and portable codes for practical applications. We describe sympy2c, a new Python package designed to bridge the gap between the symbolic development and the numerical implementation of a theoretical model. sympy2c translates symbolic equations implemented in the SymPy Python package to C/C++ code that is optimized using symbolic transformations. The resulting functions can be conveniently used as an extension module in Python. sympy2c is used within the PyCosmo Python package to solve the Einstein-Boltzmann equations, a large system of ODEs describing the evolution of linear perturbations in the Universe. After reviewing the functionalities and usage of sympy2c, we describe its implementation and optimization strategies. This includes, in particular, a novel approach to generate optimized ODE solvers making use of the sparsity of the symbolic Jacobian matrix. We demonstrate its performance using the Einstein-Boltzmann equations as a test case. sympy2c is widely applicable and may prove useful for various areas of computational physics. sympy2c is publicly available at https://cosmology.ethz.ch/research/software-lab/sympy2c.html

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arXiv2022

The Hydrogen Intensity and Real-time Analysis eXperiment: 256-Element Array Status and Overview

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is a radio interferometer array currently in development, with an initial 256-element array to be deployed at the South African Radio Astronomy Observatory (SARAO) Square Kilometer Array (SKA) site in South Africa. Each of the 6m, $f/0.23$ dishes will be instrumented with dual-polarisation feeds operating over a frequency range of 400-800 MHz. Through intensity mapping of the 21 cm emission line of neutral hydrogen, HIRAX will provide a cosmological survey of the distribution of large-scale structure over the redshift range of $0.775 < z < 2.55$ over $\sim$15,000 square degrees of the southern sky. The statistical power of such a survey is sufficient to produce $\sim$7 percent constraints on the dark energy equation of state parameter when combined with measurements from the Planck satellite. Additionally, HIRAX will provide a highly competitive platform for radio transient and HI absorber science while enabling a multitude of cross-correlation studies. In this paper, we describe the science goals of the experiment, overview of the design and status of the sub-components of the telescope system, and describe the expected performance of the initial 256-element array as well as the planned future expansion to the final, 1024-element array.

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