arXiv subjects

Florent Leclercq

Publications and source records attributed to Florent Leclercq.

At least 19 recordsLinked to original sources

arXiv2026

Application of Bayesian Statistical Tools to SKA Telescopes Polarization Surveys to Study Magnetization of the Large-scale Structure of the Universe

Understanding cosmological magnetic fields requires a detailed knowledge of magnetism in the different environments of the large-scale structure of the Universe. Magnetic fields are well known to inhabit galaxy clusters, and recently their presence has been detected between galaxy clusters, along filaments extending up to 10-15 Mpc. Beyond that, there is limited information on the existence of magnetic fields in sheets and voids of the cosmic web. We propose a Bayesian statistical approach to study magnetic fields on large scales through observations of the Faraday rotation effect in large samples of polarized point-like background radio sources. We present the expectations to detect magnetization in environments of the large-scale structure with the SKA-Mid polarization survey planned by the SKAO Magnetism Science Working Group and with SKA-Low with AA4 telescopes, and discuss the required level of accuracy on the redshifts of the host galaxies for such a study. We find that about 50,000 mid-frequency Faraday rotation measurements complemented by high-precision redshifts are needed to constrain magnetization of dense environments as galaxy clusters. Investigation of magnetization in weakly-magnetized low-density enviroments, as filaments, will remain challenging, but low frequencies radio observations and spectroscopic redhifts for at least 17,000 will allow us to put first constraints.

astro-ph.IM↗

arXiv2026

KiDS+VIKING-450 cosmology with Bayesian hierarchical model redshift distributions

Tomographic redshift distributions from photometric data are crucial ingredients in cosmic shear analysis, since they are required for the theoretical calculation of the signal based on the redshift distribution of the galaxies where the shear field is sampled. In this paper, we develop as a proof of concept Leistedt et al.'s template-based Bayesian Hierarchical Model framework into an application to weak lensing data by sampling the redshift distributions of the galaxies in the KiDS+VIKING-450 survey. We also use a principal component analysis to provide a set of representative templates drawn from a large superset. For computational tractability, subsets of $10^5$ galaxies are chosen to determine the redshift distributions, and we test the sensitivity of the cosmological inference to the subset chosen, finding it to be subdominant compared to the statistical error. We marginalise over the inferred redshift distributions and find that the Bayesian method increases the clustering parameter compared with previous studies, alleviating the $S_8$ tension with Planck, where $S_{8}\equiv\sigma_{8}\sqrt{\Omega_{\tm{m}}/0.3}=0.756\pm 0.039$, assuming flat $\Lambda$CDM. The tension with Planck for this survey is reduced from $2.3\sigma$ to $1.9\sigma$. We also infer a value for the matter density, $\Omega_{\tm{m}}=0.31\pm 0.10$.

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arXiv2026

Field-Level Inference of Primordial Non-Gaussianity with the Quijote Simulation Suite

Local primordial non-Gaussianity, parameterised as $f_{\rm NL}^{\rm local}$, will be stringently constrained using state-of-the-art methods applied to next-generation galaxy redshift survey data. In this paper, in preparation for the upcoming data sets, we demonstrate for the first time the joint field-level inference of $f_{\rm NL}^{\rm local}$, nuisance parameters, and the initial conditions in realistic halo catalogues, ones which are generated through full dark-matter-only $N$-body simulations. The field-level inference algorithm optimally constrains $f_{\rm NL}^{\rm local}$ through a Bayesian forward-modelling approach at the field level, which outperforms traditional methods by leveraging the full statistical power of the data at the scales considered. In addition, we assess its performance under various design choices in the forward model, including tests of the structure formation model and resolution. We demonstrate the robustness of our approach by applying it to a subset of the \textit{Quijote} simulation suite, performing the inference at scales down to $k_{\rm max} \approx 0.1 h \rm{Mpc}^{-1}$. Compared with a power spectrum and bispectrum estimator, we find a $\sim1.3$ improvement in $\sigma(f_{\rm NL}^{\rm local})$ when applying \borg{}, while marginalising over the initial conditions and bias parameters. From the small-scale information sensitivity tests, we show that the constraints on $f_{\rm NL}^{\rm local}$ improve as we increase the resolution of the inference. These findings underscore the transformative potential of field-level inference to leverage the information available in ongoing surveys such as \textit{Euclid}, providing accurate insights into the physics of cosmic inflation and the number of fields driving it.

astro-ph.CO↗

arXiv2026

Reconstructing the largest scales of the Universe with field-level inference applied to the Quaia Quasar Catalogue

The recently released Quaia quasar catalogue, with its broad redshift range and all-sky coverage, enables unprecedented three-dimensional reconstructions of matter across cosmic time. In this work, we apply the field-level inference algorithm BORG to the Quaia catalogues to reconstruct the initial conditions and present-day matter distribution of the Universe. We employ a physics-based forward model of large-scale structure using Lagrangian perturbation theory, incorporating light-cone effects, redshift-space distortions, quasar bias, and survey selection effects. This approach enables a detailed and physically motivated inference of the three-dimensional density field and initial conditions over the entire cosmic volume considered. We analyse both the G < 20.0 (Quaia Clean) and G < 20.5 (Quaia Deep) samples, where G denotes the Gaia broad optical-band magnitude, imposing conservative sky cuts to ensure robustness against foreground contamination. The resulting reconstructions span a comoving volume of (10h^{-1} Gpc)^3 with a maximum spatial resolution of 39.1 h^{-1}Mpc, making this the largest field-level reconstruction of the observable Universe in terms of comoving volume to date. We validate our reconstructions through a range of internal and external consistency checks, including the cross-correlation of the inferred density fields with Planck CMB lensing, where we detect a signal at ~4\sigma significance. Beyond delivering high-fidelity data products, including posterior maps of initial conditions, present-day dark matter, and velocity fields, this work establishes a framework for exploiting quasar surveys in field-level cosmology.

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arXiv2025

Field-level inference in cosmology

These lecture notes delve into field-level inference, a framework offering a robust way to extract more information and avoid biases compared to traditional methods for cosmological data analysis. The core idea is to analyse uncompressed maps to infer underlying physical fields and cosmological parameters. We introduce Bayesian hierarchical field-level models and discuss sampling techniques for exploring complex, high-dimensional posterior distributions. We review the framework that underpins field-level inference. Finally, we highlight some state-of-the-art applications across various cosmological probes, and the growing role of machine learning in enhancing field-level inference capabilities.

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arXiv2025

COmoving Computer Acceleration (COCA): $N$-body simulations in an emulated frame of reference

$N$-body simulations are computationally expensive, so machine-learning (ML)-based emulation techniques have emerged as a way to increase their speed. Although fast, surrogate models have limited trustworthiness due to potentially substantial emulation errors that current approaches cannot correct for. To alleviate this problem, we introduce COmoving Computer Acceleration (COCA), a hybrid framework interfacing ML with an $N$-body simulator. The correct physical equations of motion are solved in an emulated frame of reference, so that any emulation error is corrected by design. This approach corresponds to solving for the perturbation of particle trajectories around the machine-learnt solution, which is computationally cheaper than obtaining the full solution, yet is guaranteed to converge to the truth as one increases the number of force evaluations. Although applicable to any ML algorithm and $N$-body simulator, this approach is assessed in the particular case of particle-mesh cosmological simulations in a frame of reference predicted by a convolutional neural network, where the time dependence is encoded as an additional input parameter to the network. COCA efficiently reduces emulation errors in particle trajectories, requiring far fewer force evaluations than running the corresponding simulation without ML. We obtain accurate final density and velocity fields for a reduced computational budget. We demonstrate that this method shows robustness when applied to examples outside the range of the training data. When compared to the direct emulation of the Lagrangian displacement field using the same training resources, COCA's ability to correct emulation errors results in more accurate predictions. COCA makes $N$-body simulations cheaper by skipping unnecessary force evaluations, while still solving the correct equations of motion and correcting for emulation errors made by ML.

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arXiv2024

Bayesian Inference of Initial Conditions from Non-Linear Cosmic Structures using Field-Level Emulators

Analysing next-generation cosmological data requires balancing accurate modeling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full $N$-body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from $N$-body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of $1.95h^{-1}$ Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency $k_\mathrm{N} \approx 2.79h \; \mathrm{Mpc}^{-1}$. Posterior resimulations - using the inferred initial conditions for $N$-body simulations - show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate $M_{200\mathrm{c}}$ halo mass function and stacked density profiles of haloes in different mass bins $[0.853,16]\times 10^{14}M_{\odot}h^{-1}$. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modeling, indicating the potential for extracting additional information at smaller scales.

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arXiv2024

Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum

The next generation of galaxy surveys has the potential to substantially deepen our understanding of the Universe. This potential hinges on our ability to rigorously address systematic uncertainties. Until now, diagnosing systematic effects prior to inferring cosmological parameters has been out of reach in field-based implicit likelihood cosmological inference frameworks. As a solution, we aim to diagnose a variety of systematic effects in galaxy surveys prior to inferring cosmological parameters, using the inferred initial matter power spectrum. Our approach is built upon a two-step framework. First, we employed the SELFI algorithm to infer the initial matter power spectrum, which we utilised to thoroughly investigate the impact of systematic effects. This investigation relies on a single set of $N$-body simulations. Second, we obtained a posterior on cosmological parameters via implicit likelihood inference, recycling the simulations from the first step for data compression. For demonstration, we relied on a model of large-scale spectroscopic galaxy surveys that incorporates fully non-linear gravitational evolution with COLA and simulates multiple systematic effects encountered in real surveys. We provide a practical guide on how the SELFI posterior can be used to assess the impact of misspecified galaxy bias parameters, selection functions, survey masks, inaccurate redshifts, and approximate gravity models on the inferred initial matter power spectrum. We show that a subtly misspecified model can lead to a bias exceeding $2\sigma$ in the $(\Omega_\mathrm{m}, \sigma_8)$ plane, which we are able to detect and avoid prior to inferring the cosmological parameters. This framework has the potential to significantly enhance the robustness of physical information extraction from full forward models of large-scale galaxy surveys such as DESI, Euclid, and LSST.

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arXiv2024

Luminous giants populate the dense Cosmic Web: The radio luminosity-environmental density relation for radio galaxies in action

Giant radio galaxies (GRGs, giant RGs, or giants) are megaparsec-scale, jet-driven outflows from accretion disks of supermassive black holes, and represent the most extreme pathway by which galaxies can impact the Cosmic Web around them. A long-standing but unresolved question is why giants are so much larger than other radio galaxies. It has been proposed that, in addition to having higher jet powers than most RGs, giants might live in especially low-density Cosmic Web environments. In this work, we aim to test this hypothesis by pinpointing Local Universe giants and other RGs in physically principled, Bayesian large-scale structure reconstructions. More specifically, we localised a LOFAR Two-metre Sky Survey (LoTSS) DR2-dominated sample of luminous ($l_ν(ν= 150\ \mathrm{MHz}) \geq 10^{24}\ \mathrm{W\ Hz^{-1}}$) giants and a control sample of LoTSS DR1 RGs, both with spectroscopic redshifts up to $z_\mathrm{max} = 0.16$, in the BORG SDSS Cosmic Web reconstructions. We measured the Cosmic Web density for each RG; for the control sample, we then quantified the relation between RG radio luminosity and Cosmic Web density. With the BORG SDSS tidal tensor, we also measured for each RG whether the gravitational dynamics of its Cosmic Web environment resemble those of clusters, filaments, sheets, or voids. Luminous giants populate large-scale environments that tend to be denser than those of general RGs. This shows that -- at least at high jet powers -- low-density environments are no prerequisite for giant growth. This result is corroborated by gravitational dynamics classification and a cluster catalogue crossmatching analysis. This work presents more than a thousand inferred megaparsec-scale densities around radio galaxies. Our findings are consistent with the view that giants are regular, rather than mechanistically special, members of the radio galaxy population.

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arXiv2023

Higher-order statistics of the large-scale structure from photometric redshifts

The large-scale structure is a major source of cosmological information. However, next-generation photometric galaxy surveys will only provide a distorted view of cosmic structures due to large redshift uncertainties. To address the need for accurate reconstructions of the large-scale structure in presence of photometric uncertainties, we present a framework that constrains the three-dimensional dark matter density jointly with galaxy photometric redshift probability density functions (PDFs), exploiting information from galaxy clustering. Our forward model provides Markov Chain Monte Carlo realizations of the primordial and present-day dark matter density, inferred jointly from data. Our method goes beyond 2-point statistics via field-level inference. It accounts for all observational uncertainties and the survey geometry. We showcase our method using mock catalogs that emulate next-generation surveys with a worst-case redshift uncertainty, equivalent to ${\sim}300$ Mpc. On scales $150$ Mpc, we improve the cross-correlation of the photometric galaxy positions with the ground truth from $28\%$ to $86\%$. The improvement is significant down to $13$ Mpc. On scales $150$ Mpc, we achieve a cross-correlation of $80-90\%$ with the ground truth for the dark matter density, radial peculiar velocities, tidal shear and gravitational potential.

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arXiv2022

Field-Based Physical Inference From Peculiar Velocity Tracers

We present a Bayesian hierarchical modelling approach to reconstruct the initial cosmic matter density field constrained by peculiar velocity observations. As our approach features a model for the gravitational evolution of dark matter to connect the initial conditions to late-time observations, it reconstructs the final density and velocity fields as natural byproducts. We implement this field-based physical inference approach by adapting the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm, which explores the high-dimensional posterior through the use of Hamiltonian Monte Carlo sampling. We test the self-consistency of the method using random sets of mock tracers, and assess its accuracy in a more complex scenario where peculiar velocity tracers are non-linearly evolved mock haloes. We find that our framework self-consistently infers the initial conditions, density and velocity fields, and shows some robustness to model mis-specification. As compared to the state-of-the-art approach of constrained Gaussian random fields/Wiener filtering, our method produces more accurate final density and velocity field reconstructions. It also allows us to constrain the initial conditions by peculiar velocity observations, complementing in this aspect previous field-based approaches based on other cosmological observables.

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arXiv2022

An intergalactic medium temperature from a giant radio galaxy

The warm-hot intergalactic medium (warm-hot IGM, or WHIM) pervades the filaments of the Cosmic Web and harbours half of the Universe's baryons. The WHIM's thermodynamic properties are notoriously hard to measure. Here we estimate a galaxy group - WHIM boundary temperature using a new method. In particular, we use a radio image of the giant radio galaxy (giant RG, or GRG) created by NGC 6185, a massive nearby spiral. We analyse this extraordinary object with a Bayesian 3D lobe model and deduce an equipartition pressure $P_\mathrm{eq} = 6 \cdot 10^{-16}\ \mathrm{Pa}$ -- among the lowest found in RGs yet. Using an X-ray-based statistical conversion for Fanaroff-Riley II RGs, we find a true lobe pressure $P = 1.5\substack{+1.7\\-0.4}\cdot 10^{-15}\ \mathrm{Pa}$. Cosmic Web reconstructions, group catalogues, and MHD simulations furthermore imply an $\mathrm{Mpc}$-scale IGM density $1 + δ_\mathrm{IGM} = 40\substack{+30\\-10}$. The buoyantly rising lobes are crushed by the IGM at their inner side, where an approximate balance between IGM and lobe pressure occurs: $P_\mathrm{IGM} \approx P$. The ideal gas law then suggests an IGM temperature $T_\mathrm{IGM} = 11\substack{+12\\-5} \cdot 10^6\ \mathrm{K}$, or $k_\mathrm{B}T_\mathrm{IGM} = 0.9\substack{+1.0\\-0.4}\ \mathrm{keV}$, at the virial radius -- consistent with X-ray-derived temperatures of similarly massive groups. Interestingly, the method is not performing at its limit: in principle, estimates $T_\mathrm{IGM} \sim 4 \cdot 10^6\ \mathrm{K}$ are already possible -- rivalling the lowest X-ray measurements available. The technique's future scope extends from galaxy group outskirts to the WHIM. In conclusion, we demonstrate that observations of GRGs in Cosmic Web filaments are finally sensitive enough to probe the thermodynamics of galaxy groups and beyond.

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arXiv2022

Rubin-Euclid Derived Data Products: Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology.

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arXiv2022

Simulation-based inference of Bayesian hierarchical models while checking for model misspecification

This paper presents recent methodological advances to perform simulation-based inference (SBI) of a general class of Bayesian hierarchical models (BHMs), while checking for model misspecification. Our approach is based on a two-step framework. First, the latent function that appears as second layer of the BHM is inferred and used to diagnose possible model misspecification. Second, target parameters of the trusted model are inferred via SBI. Simulations used in the first step are recycled for score compression, which is necessary to the second step. As a proof of concept, we apply our framework to a prey-predator model built upon the Lotka-Volterra equations and involving complex observational processes.

stat.ME↗

arXiv2022

Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration

Existing cosmological simulation methods lack a high degree of parallelism due to the long-range nature of the gravitational force, which limits the size of simulations that can be run at high resolution. To solve this problem, we propose a new, perfectly parallel approach to simulate cosmic structure formation, which is based on the spatial COmoving Lagrangian Acceleration (sCOLA) framework. Building upon a hybrid analytical and numerical description of particles' trajectories, our algorithm allows for an efficient tiling of a cosmological volume, where the dynamics within each tile is computed independently. As a consequence, the degree of parallelism is equal to the number of tiles. We optimised the accuracy of sCOLA through the use of a buffer region around tiles and of appropriate Dirichlet boundary conditions around sCOLA boxes. As a result, we show that cosmological simulations at the degree of accuracy required for the analysis of the next generation of surveys can be run in drastically reduced wall-clock times and with very low memory requirements. The perfect scalability of our algorithm unlocks profoundly new possibilities for computing larger cosmological simulations at high resolution, taking advantage of a variety of hardware architectures.

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arXiv2022

Filamentary Baryons and Where to Find Them: A forecast of synchrotron radiation from merger and accretion shocks in the local Cosmic Web

We generate probabilistic predictions of the low-redshift ($z < 0.2$) synchrotron Cosmic Web for half of the Northern Sky. In particular, we predict the contribution to the specific intensity function at $ν_\mathrm{obs} = 150\ \mathrm{MHz}$ from merger shocks in clusters and accretion shocks in filaments, both of which arise during large-scale structure formation. We assume a primordial magnetogenesis scenario, but our method is general enough to allow for an exploration of alternative magnetogenesis scenarios - and even alternative radiation mechanisms and spectral windows. In the future, by comparing different predictions, one could infer the most plausible physical model from a synchrotron Cosmic Web detection. Our method combines Bayesian large-scale structure reconstructions, snapshots of an MHD cosmological simulation, a Gaussian random field, and a ray tracing approach. The results help to select targets for deep observations and can be used in actual detection experiments. We highlight predictions for the Hercules Cluster, the Coma Cluster, Abell 2199, Abell 2255, the Lockman Hole and the Ursa Major Supercluster. At degree-scale resolution, the median specific intensity reaches $m_{I_ν} \sim 10^{-1}\ ξ_e\ \mathrm{Jy\ deg^{-2}}$, where $ξ_e$ is the electron acceleration efficiency.

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arXiv2021

Kernel-Based Emulator for the 3D Matter Power Spectrum from CLASS

The 3D matter power spectrum, $P_δ(k,z)$ is a fundamental quantity in the analysis of cosmological data such as large-scale structure, 21cm observations, and weak lensing. Existing computer models (Boltzmann codes) such as CLASS can provide it at the expense of immoderate computational cost. In this paper, we propose a fast Bayesian method to generate the 3D matter power spectrum, for a given set of wavenumbers, $k$ and redshifts, $z$. Our code allows one to calculate the following quantities: the linear matter power spectrum at a given redshift (the default is set to 0); the non-linear 3D matter power spectrum with/without baryon feedback; the weak lensing power spectrum. The gradient of the 3D matter power spectrum with respect to the input cosmological parameters is also returned and this is useful for Hamiltonian Monte Carlo samplers. The derivatives are also useful for Fisher matrix calculations. In our application, the emulator is accurate when evaluated at a set of cosmological parameters, drawn from the prior, with the fractional uncertainty, $ΔP_δ/P_δ$ centred on 0. It is also $\sim 300$ times faster compared to CLASS, hence making the emulator amenable to sampling cosmological and nuisance parameters in a Monte Carlo routine. In addition, once the 3D matter power spectrum is calculated, it can be used with a specific redshift distribution, $n(z)$ to calculate the weak lensing and intrinsic alignment power spectra, which can then be used to derive constraints on cosmological parameters in a weak lensing data analysis problem. The software ($\texttt{emuPK}$) can be trained with any set of points and is distributed on Github, and comes with a pre-trained set of Gaussian Process (GP) models, based on 1000 Latin Hypercube (LH) samples, which follow roughly the current priors for current weak lensing analyses.

astro-ph.CO↗

arXiv2021

On the accuracy and precision of correlation functions and field-level inference in cosmology

We present a comparative study of the accuracy and precision of correlation function methods and full-field inference in cosmological data analysis. To do so, we examine a Bayesian hierarchical model that predicts log-normal fields and their two-point correlation function. Although a simplified analytic model, the log-normal model produces fields that share many of the essential characteristics of the present-day non-Gaussian cosmological density fields. We use three different statistical techniques: (i) a standard likelihood-based analysis of the two-point correlation function; (ii) a likelihood-free (simulation-based) analysis of the two-point correlation function; (iii) a field-level analysis, made possible by the more sophisticated data assimilation technique. We find that (a) standard assumptions made to write down a likelihood for correlation functions can cause significant biases, a problem that is alleviated with simulation-based inference; and (b) analysing the entire field offers considerable advantages over correlation functions, through higher accuracy, higher precision, or both. The gains depend on the degree of non-Gaussianity, but in all cases, including for weak non-Gaussianity, the advantage of analysing the full field is substantial.

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