arXiv subjects

Alexandre Refregier

Publications and source records attributed to Alexandre Refregier.

At least 19 recordsLinked to original sources

arXiv2026

Baryonification IV: Constraining baryonic feedback with X-ray gas fractions

Baryonic feedback redistributes gas around dark matter halos, suppressing the matter power spectrum at scales now probed by weak lensing surveys. X-ray observations directly trace this hot gas, and are one of the main probes of its distribution and properties. We present a forward-modelling framework, built on the baryonification model, linking the three-dimensional gas density and temperature profiles of groups and clusters to observed X-ray surface brightness and luminosity profiles on one side, and to matter power spectrum suppression on the other. We validate the model against independent three-dimensional density reconstructions from the literature, and examine our temperature and metallicity treatment in the group-scale regime. Applying this framework to the SZ-selected CHEX-MATE and X-ray-selected eFEDs samples, we measure gas fractions across the group-to-cluster mass range while accounting for X-ray selection effects, with the first published gas fractions based on CHEX-MATE data. Combining both samples, we derive a joint constraint on the hot gas fraction retained by groups and clusters as a function of mass and on the baryonic suppression of the matter power spectrum. We find $f_{\rm gas} = 0.029 \pm 0.006$ at $M_{500c} = 3\times 10^{13}M_\odot$, $f_{\rm gas} = 0.078 \pm 0.004$ at $M_{500c} = 3\times 10^{14}M_\odot$, and suppression of 6% at $k=1\,h/\rm Mpc$ and 23% at $k=5\,h/\rm Mpc$. Our findings are consistent with recent kinematic Sunyaev-Zel'dovich results, hinting at strong feedback. We also show that the $L_X$-$M$ relation is degenerate with feedback strength, and that different feedback scenarios produce distinct X-ray profile shapes that map onto the same $L_X$-$M$ point. This work is a first step toward extending the framework to forward-model diffuse X-ray emission at the map level for simulation-based inference in upcoming wide-area X-ray surveys such as eROSITA.

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arXiv2026

Dish Assembly Precision for HIRAX

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is a radio interferometer array that is being deployed at the South African Radio Astronomy Observatory (SARAO) Square Kilometer Array (SKA) site in South Africa. Mapping the southern sky, its aim is to observe neutral hydrogen (HI) through intensity mapping (IM) across the redshift range of 0.78-2.55. The observation of HI makes it possible to tomographically probe large cosmological volumes, enabling constraints on, for example, the dark energy equation of state. Systematics are a significant concern in deriving cosmological constraints from HI IM due to the presence of strong foreground signals. Instrumental effects such as dish surface deviations and feed placement errors need to be carefully controlled and monitored to preserve the sensitivity of HIRAX's redundant array configuration. These instrumental systematics cause bright foreground power to leak into the faint cosmological signal. The 6 m parabolic dishes are made from fiberglass with an embedded aluminum mesh that acts as the reflector. The feed is held in place at the prime focus (f/D = 0.21) by four fiberglass legs. This paper presents the dish surface and feed placement precision of the first 28 dishes, derived from photogrammetry metrology measurements taken during the dish fabrication, as well as from measurements in the field. While surface deviations are predominantly well within requirements, feed placement satisfies all accuracy criteria but fails precision limits; however, precision is expected to improve with a larger sample size. These results establish a baseline for modeling primary beam effects, ultimately enabling the characterization and mitigation of systematic errors in the HI IM signal with the full HIRAX array.

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arXiv2026

GalSBI: Forward Modelling Galaxy Clustering and Population

Forward modelling is a powerful approach for analyzing large-scale structure surveys. For this purpose, we extend the GalSBI framework to jointly model the galaxy population and clustering using an efficient subhalo abundance matching scheme based on optimal transport. We use simulation-based inference to constrain the model parameters by comparing UFig image simulations with DES Y3 imaging data. As a validation, we find that galaxy photometry and morphology agree well with multi-band imaging data of different depths, namely DES and HSC deep fields. Galaxy clustering for simulation and data is also in good agreement when comparing the angular power spectrum for different magnitude and color cuts. We further compare simulated redshift distributions against high-precision photometric redshifts in HSC deep field imaging of the COSMOS field. We find the redshift distributions across magnitude cuts to be similar to previous work, however with more realistic uncertainty modelling due to the addition of clustering contribution to sample variance. The agreement of the mean redshifts with data is very good, between $0.2\sigma$ and $1.6\sigma$ for different magnitude cuts, with sample variance being the dominant uncertainty contributor in bright samples ($<24$ mag) and subdominant compared to galaxy population model uncertainty in fainter samples. As a byproduct we measure the galaxy luminosity function and galaxy-halo connection, which are broadly consistent with existing literature. The updated GalSBI code and galaxy population model are publicly available. They enable accurate forward-modelled image simulations with realistic clustering, which can be used to model the effect of sample variance, source clustering, redshift distributions, and blending in large-scale-structure surveys. This makes GalSBI a powerful tool for the analysis of current and next-generation cosmological galaxy surveys.

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arXiv2026

Simulations of the 21cm emission line for upcoming large-scale HI galaxy surveys

Upcoming galaxy surveys with the SKA Observatory will detect neutral hydrogen (HI) across unprecedented volumes, and their scientific return will crucially depend on predictive models for HI observables. In this work, we present a framework to simulate the neutral hydrogen 21cm emission line in such large-scale HI galaxy surveys. This framework is developed as a modular layer that builds on semi-analytical models. In particular we use as bases the Galaxy Evolution and Assembly (GAEA) and L-Galaxies semi-analytical models, coupled to merger trees from the Millennium Simulation suite. We validate our framework against local Universe observations, demonstrating consistency with velocity functions, and generalised Tully-Fisher relations. Predictions based on GAEA and L-Galaxies exhibit mutual consistency despite the distinct underlying physical prescriptions. We construct mock galaxy catalogues that incorporate forward-modelled selection functions, inclination effects, and redshift broadening, reproducing the statistical distributions of HI-selected galaxies in the ALFALFA survey. Finally, we present redshift distribution forecasts for future SKA Observatory HI galaxy surveys. This framework offers a flexible tool for interpreting upcoming large-scale radio surveys and studying HI line observables as cosmological probes.

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arXiv2026

Baryonification III: An accurate analytical model for the dispersion measure probability density function of fast radio bursts

We develop an analytical framework to predict the one-point probability distribution function (PDF) of dispersion measures (DMs) for fast radio bursts (FRBs) within the baryonification (BFC) model. BFC provides a computationally efficient alternative to expensive hydrodynamical simulations for modelling baryonic effects on cosmological scales. By applying the halo mass function and halo bias, we convolve contributions from individual halos across a range of masses and redshifts to derive the large-scale structure contribution to the DM PDF. We validate our analytical predictions against consistency-check simulations and compare them with the IllustrisTNG hydrodynamical simulation over the redshift range $ z = 0$ to $z = 5$, demonstrating excellent agreement. We demonstrate that our model produces consistent results when fitting gas profiles and predicting the PDF, and vice versa. We show that the BFC parameters controlling the gas profile, particularly the halo mass scale ($M_\mathrm{c}$), mass-dependent slope ($\mu$), and outer truncation ($\delta$), are the primary drivers of the PDF shape. Additionally, we investigate the validity of the log-normal approximation commonly used for DM distributions, finding that it provides a sufficient description for a few hundred FRBs. Our work provides a self-consistent model that links gas density profiles to integrated DM statistics, enabling future constraints on baryonic feedback processes from FRB observations.

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arXiv2025

The cosmological analysis of DES 3$\times$2pt data from the Effective Field Theory of Large-Scale Structure

We analyze the Dark Energy Survey (DES) Year 3 data using predictions from the Effective Field Theory of Large-Scale Structure (EFTofLSS). Specifically, we fit three two-point observables (3$\times$2pt), galaxy clustering, galaxy-galaxy lensing, and cosmic shear, using the one-loop expressions for the projected angular correlation functions. We validate our pipeline against numerical simulations and we check for several internal consistencies before applying it to the observational data. Fixing the spectral tilt and the baryons abundance, we measure $S_8=0.833\pm 0.032$, $\Omega_m = 0.272\pm 0.022$, and $h = 0.773\pm 0.049$, to about $3.8\%$, $8.1\%$, and $6.3\%$, at $68\%$CL, respectively. Our results are consistent at the $\sim 1.5-2\sigma$ level with those from Planck and the BOSS full-shape analyses, as well as with those from DES collaboration 3$\times$2pt analysis combined with a Big-Bang Nucleosynthesis prior and a Planck prior on $n_s$. The shift in the posterior compared to DES collaboration results highlights the impact of modeling, scale cuts, and choice of prior. The theory code and likelihood used for our analyses, \texttt{PyFowl}, is made publicly available.

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arXiv2025

Multiprobe constraints on early and late time dark energy

We perform a multiprobe analysis combining cosmic microwave background (CMB) data from Planck and the Atacama Cosmology Telescope (ACT), ACT CMB lensing, and large-scale structure (LSS) measurements from the Dark Energy Spectroscopic Instrument (DESI), including DESI Legacy Imaging Survey (LS) galaxies and baryon acoustic oscillations (BAOs). We present the first $5\times2$pt analysis of ACT DR6 lensing, DESI LS, and Planck ISW. Within $\Lambda$CDM, this yields $S_8 = \sigma_8(\Omega_m/0.3)^{0.5} = 0.819 \pm 0.016$, in good agreement with primary CMB inferences and provides a sound-horizon-free Hubble constant constraint of $H_0 = 70.0 \pm 4.4$ km s$^{-1}$ Mpc$^{-1}$. Then, combining with CMB primary and BAO, we reconfirm a CMB-BAO discrepancy in the $\Omega_m$-$\frac{D_v}{r_d}$ plane, which is heightened when combining BAO with the $5\times2$pt data vector. We explore two dark-energy extensions that may reconcile this: an early-time modification, early dark energy (EDE), and late-time dynamical dark energy (DDE) parameterized by $w_0w_a$. For CMB primary+BAO+$5\times2$pt, we find a $3.3\sigma$ preference for DDE over $\Lambda$CDM, while EDE is modestly favoured at $2.3\sigma$. The models address different shortcomings of $\Lambda$CDM: DDE relaxes the neutrino mass bound ($M_\nu<0.17$eV vs. $<0.050$eV under $\Lambda$CDM), making it compatible with neutrino oscillation measurements, while EDE raises the Hubble constant to $H_0=70.5\pm1.2\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$, easing the discrepancy with SH0ES. However, neither model resolves both issues simultaneously. Our analysis indicates that both DDE and EDE remain viable extensions of $\Lambda$CDM within current uncertainties and demonstrates the capacity of combined probes to place increasingly stringent constraints on cosmological parameters.

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arXiv2025

ProMage: fast galaxy magnitudes emulation combining SED forward-modelling and machine learning

We present ProMage, a feed-forward neural network that emulates the computation of observer- and rest-frame magnitudes from the generative galaxy SED package ProSpect. The network predicts magnitudes conditioned on input galaxy physical properties, including redshift, star formation history, gas and dust parameters. ProMage accelerates magnitude computation by a factor of $10^4$ compared to ProSpect, while achieving per-mille relative accuracy for $99\%$ of sources in the test set across the $g,r,i,z,y$ Hyper Suprime-Cam bands. This acceleration is key to enabling fast inference of galaxy physical properties in next-generation Stage IV surveys and to generating large catalogue realisations in forward-modelling frameworks such as GalSBI-SPS.

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arXiv2025

Baryonification: An alternative to hydrodynamical simulations for cosmological studies

We present an improved baryonification (BFC) model that modifies dark-matter-only $N$-body simulations to generate particle-level outputs for gas, dark matter, and stars. Unlike previous implementations, our approach first splits each simulation particle into separate dark matter and baryonic components, which are then displaced individually using the BFC technique. By applying the hydrostatic and ideal gas equations, we assign pressure and temperature values to individual gas particles. The model is validated against hydrodynamical simulations from the FLAMINGO and TNG suites (which feature varied feedback prescriptions) showing good agreement at the level of density and pressure profiles across a wide range of halo masses. As a further step, we calibrate the BFC model parameters to gas and stellar mass ratio profiles from the hydrodynamical simulations. Based on these calibrations, we baryonify $N$-body simulations and compare the resulting total matter power spectrum suppressions to the ones from the same hydrodynamical simulation. Carrying out this test of the BFC method at each redshift individually, we obtain a 2 percent agreement up to $k=5\,h$/Mpc across all tested feedback scenarios. We also define a reduced, 2+1 parameter BFC model that simultaneously accounts for feedback variations (2 parameters) and redshift evolution (1 parameter). The 2+1 parameter model agrees with the hydrodynamical simulations to better than 2.5 percent over the scales and redshifts relevant for cosmological surveys. Finally, we present a map-level comparison between a baryonified $N$-body simulation and a full hydrodynamical run from the TNG simulation suite. Visual inspection of dark matter, gas, and stellar density fields, along with the integrated pressure map, shows promising agreement. Further work is needed to quantify the accuracy at the level of observables.

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arXiv2025

$\texttt{SwiftC}_\ell$: fast differentiable angular power spectra beyond Limber

The upcoming stage IV wide-field surveys will provide high precision measurements of the large-scale structure (LSS) of the universe. Their interpretation requires fast and accurate theoretical predictions including large scales. For this purpose, we introduce $\texttt{SwiftC}_\ell$, a fast, accurate and differentiable $\texttt{JAX}$-based pipeline for the computation of the angular power spectrum beyond the Limber approximation. It uses a new FFTLog-based method which can reach arbitrary precision and includes interpolation along $k$, allowing for $k$-dependent growth factor and biases. $\texttt{SwiftC}_\ell$ includes a wide range of probes and effects such as galaxy clustering, including magnification bias, redshift-space distortions and primordial non-Gaussianity, weak lensing, including intrinsic alignment, cosmic microwave background (CMB) lensing and CMB integrated Sachs-Wolfe effect. We compare our pipeline to the other available beyond-Limber codes within the N5K challenge from the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration. $\texttt{SwiftC}_\ell$ computes the 120 different angular power spectra over 103 $\ell$-multipoles in 5 ms on one GPU core while the computation of the gradient is approximately 4$\times$ slower. Using a pre-calculation, $\texttt{SwiftC}_\ell$ is thus about 40$\times$ faster than the winner of the N5K challenge with comparable accuracy. Furthermore, all outputs are auto-differentiable, facilitating gradient-based sampling and robust and accurate Fisher forecasts. We showcase a Markov Chain Monte Carlo, a Hamiltonian Monte Carlo and a Fisher forecast on an LSST-like survey, illustrating $\texttt{SwiftC}_\ell$'s differentiability, speed and reliability in measuring cosmological parameters. The code is publicly available at https://cosmo-gitlab.phys.ethz.ch/cosmo_public/swiftcl.

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arXiv2025

GalSBI-SPS: a stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications

Next generation photometric and spectroscopic surveys will enable unprecedented tests of the concordance cosmological model and of galaxy formation and evolution. Fully exploiting their potential requires a precise understanding of the selection effects on galaxies and biases on measurements of their properties, required, above all, for accurate estimates of redshift distributions n(z). Forward-modelling offers a powerful framework to simultaneously recover galaxy $n(z)$s and characterise the observed galaxy population. We present GalSBI-SPS, a new SPS-based galaxy population model that generates realistic galaxy catalogues, which we use to forward-model HSC data in the COSMOS field. GalSBI-SPS samples galaxy physical properties, computes magnitudes with ProSpect, and simulates HSC images in the COSMOS field with UFig. We measure photometric properties consistently in real data and simulations. We compare $n(z)$s, photometric and physical properties to observations and to GalSBI. GalSBI-SPS reproduces the observed grizy magnitude, colour, and size distributions down to i<23. Median differences in magnitudes and colours remain below 0.14 mag, with the model covering the full colour space spanned by HSC. Galaxy sizes are overestimated by 0.2 arcsec on average and some tension exists in the g-r colour, but the latter is comparable to that seen in GalSBI. $n(z)$s show a mild positive offset (0.01-0.08) in the mean. GalSBI-SPS qualitatively reproduces the stellar mass-SFR and size-stellar mass relations seen in COSMOS2020. GalSBI-SPS provides a realistic, survey-independent galaxy population description at a Stage-III depth using only literature-based parameters. Its predictive power will improve significantly when constrained against observed data using SBI, thereby providing accurate $n(z)$s satisfying the stringent requirements set by Stage IV surveys.

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arXiv2025

SHAM-OT: Rapid Subhalo Abundance Matching with Optimal Transport

Subhalo abundance matching (SHAM) is widely used for connecting galaxies to dark matter haloes. In SHAM, galaxies and (sub-)haloes are sorted according to their mass (or mass proxy) and matched by their rank order. In this work, we show that SHAM is the solution of the optimal transport (OT) problem on empirical distributions (samples or catalogues) for any metric transport cost function. In the limit of large number of samples, it converges to the solution of the OT problem between continuous distributions. We propose SHAM-OT: a formulation of abundance matching where the halo-galaxy relation is obtained as the optimal transport plan between galaxy and halo mass functions. By working directly on these (discretized) functions, SHAM-OT eliminates the need for sampling or sorting and is solved using efficient OT algorithms at negligible compute and memory cost. Scatter in the galaxy-halo relation can be naturally incorporated through regularization of the transport plan. SHAM-OT can easily be generalized to multiple marginal distributions. We validate our method using analytical tests with varying cosmology and luminosity function parameters, and on simulated halo catalogues. The efficiency of SHAM-OT makes it particularly advantageous for Bayesian inference that requires marginalization over stellar mass or luminosity function uncertainties.

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arXiv2025

Tuning the cosmic instrument: robust cosmology through combined probes

As wide-field surveys yield increasingly precise data, multiprobe analyses offer significant advantages. In this work, we use our previously developed framework for jointly analyzing cosmic microwave background (CMB) and large-scale structure data. We analyze combinations of three CMB (Planck PR3, Planck PR4, and ACT+WMAP) datasets, DESI Y1 Baryon Acoustic Oscillation (BAO) data, and a $9\times 2$pt low-$z$ dataset comprising KiDS-1000, BOSS DR12, and Planck CMB lensing/Integrated Sachs Wolfe (including all cross-correlations). We first assess internal consistency, finding a mild ($<2\sigma$) tension between CMB and low-$z$ datasets in the full parameter space and hints of systematics in Planck PR3 and KiDS-1000. We then derive constraints in $\Lambda\mathrm{CDM}$ and, motivated by recent DESI results, dynamical dark energy ($w_0w_a\mathrm{CDM}$) and free neutrino mass extensions. In $\Lambda \mathrm{CDM}$, we derive a novel $9\times2$pt constraint of $S8=0.777^{+0.17}_{-0.17}$ and find strong consistency among CMB datasets. In $w_0w_a\mathrm{CDM}$, adding low-$z$ to CMB+BAO tightens $(w_0,w_a)$ constraints by 50\% (in figure-of-merit terms) in our baseline combination of Planck PR4 + low-$z$ + BAO. The posterior accommodates a cosmological constant ($w_0 = -1, w_a = 0$) within $1\sigma$, in contrast to the $\sim2\sigma$ preference for evolving dark energy from CMB+BAO alone. For neutrino masses, our baseline dataset yields a systematics-robust constraint of $M_\nu<0.12\mathrm{eV}$ in $\nu\Lambda\mathrm{CDM}$. Allowing dynamical dark energy and free neutrino mass ($\nu w_0w_a\mathrm{CDM}$) broadens and shifts the neutrino mass posterior higher, yielding a $1.8\sigma$ constraint ($M_\nu=0.16^{+0.09}_{-0.09}\mathrm{eV}$) in our baseline. Our analysis demonstrates the power of multiprobe analyses for assessing tensions, identifying systematics and providing robust constraints.

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arXiv2025

Interpretability of deep-learning methods applied to large-scale structure surveys

Deep learning and convolutional neural networks in particular are powerful and promising tools for cosmological analysis of large-scale structure surveys. They are already providing similar performance to classical analysis methods using fixed summary statistics, are showing potential to break key degeneracies by better probe combination and will likely improve rapidly in the coming years as progress is made in the physical modelling through both software and hardware improvement. One key issue remains: unlike classical analysis, a convolutional neural network's decision process is hidden from the user as the network optimises millions of parameters with no direct physical meaning. This prevents a clear understanding of the potential limitations and biases of the analysis, making it hard to rely on as a main analysis method. In this work, we explore the behaviour of such a convolutional neural network through a novel method. Instead of trying to analyse a network a posteriori, i.e. after training has been completed, we study the impact on the constraining power of training the network and predicting parameters with degraded data where we removed part of the information. This allows us to gain an understanding of which parts and features of a large-scale structure survey are most important in the network's prediction process. We find that the network's prediction process relies on a mix of both Gaussian and non-Gaussian information, and seems to put an emphasis on structures whose scales are at the limit between linear and non-linear regimes.

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arXiv2024

GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference

We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizations of galaxy luminosity functions, morphologies and spectral energy distributions. Model constraints are derived through iterative Approximate Bayesian Computation, by comparing Hyper Suprime-Cam deep field images with simulations which include a forward model of instrumental, observational and source extraction effects. We developed an emulator trained on image simulations using a normalizing flow. We use it to accelerate the inference by predicting detection probabilities, including blending effects and photometric properties of each object, while accounting for background and PSF variations. This enables robustness tests for all elements of the forward model and the inference. The model demonstrates excellent performance when comparing photometric properties from simulations with observed imaging data for key parameters such as magnitudes, colors and sizes. The redshift distribution of simulated galaxies agrees well with high-precision photometric redshifts in the COSMOS field within $1.5\sigma$ for all magnitude cuts. Additionally, we demonstrate how GalSBI's redshifts can be utilized for splitting galaxy catalogs into tomographic bins, highlighting its potential for current and upcoming surveys. GalSBI is fully open-source, with the accompanying Python package, $\texttt{galsbi}$, offering an easy interface to quickly generate realistic, survey-independent galaxy catalogs.

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arXiv2024

UFig v1: The ultra-fast image generator

With the rise of simulation-based inference (SBI) methods, simulations need to be fast as well as realistic. $\texttt{UFig v1}$ is a public Python package that simulates astronomical images with exceptional speed, taking approximately the same time as source extraction. This makes it particularly well-suited for SBI methods where computational efficiency is crucial. To render an image, $\texttt{UFig}$ requires a galaxy catalog, and a description of the point spread function (PSF). It can also add background noise, sample stars using the Besan\c{c}on model of the Milky Way, and run $\texttt{SExtractor}$ to extract sources from the rendered image. The extracted sources can be matched to the intrinsic catalog, flagged based on $\texttt{SExtractor}$ output and survey masks, and emulators can be used to bypass the image simulation and extraction steps. A first version of $\texttt{UFig}$ was presented in Berg\'e et al. (2013) and the software has since been used and further developed in a variety of forward modelling applications.

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arXiv2024

galsbi: A Python package for the GalSBI galaxy population model

Large-scale structure surveys measure the shapes and positions of millions of galaxies in order to constrain the cosmological model with high precision. The resulting large data volume poses a challenge for the analysis of the data, from the estimation of photometric redshifts to the calibration of shape measurements. We present GalSBI, a model for the galaxy population, to address these challenges. This phenomenological model is constrained by observational data using simulation-based inference (SBI). The $\texttt{galsbi}$ Python package provides an easy interface to generate catalogs of galaxies based on the GalSBI model, including their photometric properties, and to simulate realistic images of these galaxies using the $\texttt{UFig}$ package.

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arXiv2024

Fast Simulation of Cosmological Neutral Hydrogen based on the Halo Model

Cosmological neutral hydrogen (HI) surveys provide a promising tomographic probe of the post-reionization era and of the standard model of cosmology. Simulations of this signal are crucial for maximizing the utility of these surveys. We present a fast method for simulating the cosmological distribution of HI based on a halo model approach. Employing the approximate $\texttt{PINOCCHIO}$ code, we generate the past light cone of dark matter halos. Subsequently, the halos are populated with HI according to a HI-halo mass relation. The nature of 21 cm intensity mapping demands large-volume simulations with a high halo mass resolution. To fulfill both requirements, we simulate a past light cone for declinations between -15{\deg} and -35{\deg} in the frequency range from 700 to 800 MHz, matching HIRAX, the Hydrogen Intensity and Real-time Analysis eXperiment. We run $\texttt{PINOCCHIO}$ for a 1 h$^{-3}$Gpc$^3$ box with 6700$^3$ simulation particles. With this configuration, halos with masses as low as M$_\text{min}$ = 4.3 $\times$ 10$^{9}$M$_{\odot}$ are simulated, resulting in the recovery of more than 97% of the expected HI density. From the dark matter and HI past light cone, maps with a width of 5 MHz are created. To validate the simulations, we have implemented and present here an analytical dark matter and HI halo model in $\texttt{PyCosmo}$, a Python package tailored for theoretical cosmological predictions. We perform extensive comparisons between analytical predictions and the simulations for the mass function, mass density, power spectrum, and angular power spectrum for dark matter and HI. We find close agreement in the mass function and mass densities, with discrepancies within a few percent. For the three-dimensional power spectra and angular power spectra, we observe an agreement better than 10%.

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