arXiv subjects

Camille Avestruz

Publications and source records attributed to Camille Avestruz.

At least 19 recordsLinked to original sources

arXiv2026

Constraining Galaxy Cluster Triaxiality via Weak Lensing --- I. Preparation for the Rubin Data Beyond Leading Order

The 3D mass distributions of galaxy clusters are generally triaxial, a geometry that is difficult to constrain from projected observations. In this work, we measure the projected halo shapes of clusters from weak lensing signatures using the Cluster Lensing Mass Modeling software, a tool developed by the Dark Energy Science Collaboration to analyze data from NSF-DOE Rubin Observatory's Legacy Survey of Space and Time (LSST). We measure ensemble halo ellipticity on the plane of the sky via axis-aligned stacking and multipole expansion of weak lensing signatures. We study a precursor dataset --- the redMaPPer cluster catalog, the metacalibration shape catalog, and the Directional Neighborhood Fitting photometric redshift catalog from the Dark Energy Survey Year 3 public data release. We select clusters that have a high centering probability (>90%) of the identified central galaxy, and use the satellite galaxy distribution to determine the major-axis orientation for stacking. We extend the analysis to the second order of ellipticity in monopole and quadrupole measurements. The projected ellipticity of the cluster sample is found to be $0.307^{+0.013}_{-0.014}$ (axis ratio $0.530^{+0.016}_{-0.015}$) with extra percent-level systematics introduced mainly by miscentering. The projected cluster ellipticity shows no statistically significant dependence on redshift or richness (mass proxy). We further verify the accuracy of the cluster shape measurement using mock catalogs. This analysis is applicable to datasets from wide-area cosmic surveys such as LSST, Euclid, and the Roman Space Telescope, where larger sample sizes will lead to tighter constraints on cluster shapes.

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arXiv2026

Probabilistic characterization of blending with LSST and application to cluster lensing cosmology

Next-generation galaxy surveys, like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will deliver unprecedented depth and sky coverage, enabling precise measurements of cosmic probes such as weak lensing and galaxy clustering. However, increased imaging depth leads to significant blending of galaxy images, particularly in dense fields like galaxy clusters. This blending, exacerbated by atmospheric blurring in ground-based observations, contaminates galaxy property measurements and causes source confusion. To simultaneously capture these effects, we develop a probabilistic framework introducing the blending entropy, a metric quantifying the ambiguity in matching detected objects to true galaxies or external reference sources. Using simulated data from the DESC Data Challenge 2 (DC2), we characterize blending in LSST data and quantify its impact on cluster lensing cosmology around cosmoDC2 halos. We demonstrate that imposing a blending entropy threshold of $S_b<0.2$ effectively filters out highly blended objects (around 25%), which are especially prevalent near the survey's magnitude limit and are associated with higher errors in shape measurements and photometric redshifts. Applying this cut substantially reduces blending-induced biases in cluster lensing profiles and mass estimates, thereby mitigating systematic errors in cosmological parameters---most notably reducing tension in $σ_8$ estimates. Our method is readily generalizable to other static probes and offers a practical path forward for real data analyses, particularly when leveraging overlapping high-resolution datasets from spaced-based missions such as Euclid or the Roman Space Telescope, where these external datasets can act as reference catalogs to improve the identification of blended sources in LSST data.

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arXiv2026

Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping

Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure. Inferring shear and convergence from images is a challenging inverse problem. The prevailing approach to this task estimates shear from weighted averages of galaxy ellipticities, calibrates these estimates to account for systematic biases, and transforms them to reconstruct convergence, a multistage procedure that requires substantial computational resources and meticulous handling of statistical uncertainties. As an alternative, we propose a probabilistic approach to field-level weak lensing inference in which we train a deep neural network to directly map a multiband image to a variational distribution over the underlying tomographic shear and convergence fields. This neural posterior estimation (NPE) procedure implicitly marginalizes over nuisance variables in the cosmological forward model and does not require evaluating the likelihood function. It is also amortized, so it enables rapid posterior inference for astronomical surveys once the neural network is trained. When evaluated on synthetic images from the LSST-DESC DC2 Simulated Sky Survey, NPE produces well-calibrated variational distributions for shear and convergence that are consistent with the ground truth. We describe how maps sampled from these variational distributions could be used in a subsequent simulation-based inference procedure to approximate the posterior distribution over cosmological parameters.

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arXiv2026

Differentiable Forward Modeling for Efficient and Accurate Shear Inference

Forthcoming Stage-IV dark energy optical surveys, such as LSST, have the ambitious goal of measuring cosmological parameters at sub-percent precision. Realizing their full scientific potential requires very precise measurement of the cosmic shear signal and control of corresponding systematics. In this work, we present a modern implementation of the Bayesian shear inference framework in Schneider et al. (2015), in the case that the PSF and sky background are known. This framework automatically propagates the pixel-noise measurement error from each galaxy into the final shear estimate, and thus requires no external calibration to handle noise bias. As a first application of this new implementation, we infer the cosmic shear posterior from simulated images consisting of isolated exponential galaxies with semi-realistic noise levels. In this simplified scenario, we estimate the absolute multiplicative bias $|m|$ of our approach to be below $0.9 \times 10^{-3}\,[3σ]$ when the intrinsic distribution of galaxy properties is known, and below $1.3 \times 10^{-3}\,[3σ]$ when these distributions are inferred alongside shear. Additionally, we make progress towards the algorithm's computational feasibility in the context of modern wide-field surveys, where billions of galaxies must be processed, by leveraging differentiable forward models of galaxies, gradient-based samplers, and GPUs. Our final galaxy-fitting MCMC produces $300$ effective samples of galaxy properties in $0.45$ seconds per galaxy using a single A100 GPU. In the future, we seek to generalize our algorithm to handle selection, detection, and model shear biases so it can be applied to real survey data.

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arXiv2026

Imprints of Mass Accretion History on Galaxy Cluster Morphology

Variations in dynamical states of galaxy clusters can introduce biases and scatter in observable-mass relations. The dynamical state of a cluster is an emergent feature of its mass accretion history (MAH), it is therefore useful to constrain the MAH of the cluster. In this work, we characterize 305 massive clusters from The300 project by connecting features from their projected stellar distributions to their mass accretion histories (MAH). As a baseline, we first correlate host dark matter halo dynamical state indicators at $z=0$ with their MAH via the Spearman rank correlation coefficient $ρ_{\mathrm{sp}}$. Both substructure mass fraction and center-of-mass offset measurements correlate strongly with the MAH measured between $0.1\lesssim z\lesssim 1$. We repeat this exercise with morphological measurements of projected stellar density maps, many of which exhibit moderate correlation strength with different times in the MAH. Broadly, core morphological measurements ($r \leq 30\,\mathrm{kpc}$) correlate better with early-time MAH. Core-excised ($50\,\mathrm{kpc} \leq r \leq 1\,\mathrm{Mpc}$) morphological measurements correlate better with late-time MAH. We further quantify the MAH prediction power of both traditional dynamical state indicators and morphological parameters using Multivariable Conditional Abundance Matching (MultiCAM). MultiCAM employs simple rank-ordering operations, making it straightforward to translate to observed datasets. We find reasonable ($ρ_{\mathrm{sp}} \geq 0.6$) performance for predictions of the mass fraction between $1\lesssim z\lesssim 0.1$, though with notable information loss when using projected quantities. In one example application of our methodology, we use the coefficients of the MultiCAM models to select subsamples of galaxy clusters that have accreted more (or less) of their $z = 0$ mass budget over a given time frame.

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arXiv2026

Neural Posterior Estimation for Inferring Weak Lensing Shear

The prevailing approach to inferring weak gravitational lensing shear from images involves detecting galaxies, estimating their ellipticities, and calibrating these estimates to correct for image noise, selection bias, and model misspecification. Characterizing the statistical model and assumptions underlying this pipeline is challenging, which makes it difficult to propagate uncertainty through its various stages. As an alternative, we propose to infer shear using neural posterior estimation (NPE), a type of simulation-based inference. We train a deep neural network to map a simulated multiband image to a variational distribution over the underlying shear field, thereby folding galaxy detection, deblending, measurement, and calibration into a single implicit inference step. Once trained, the network accounts for all features present in the simulated images, including potential sources of bias. In experiments on simulated constant-shear images with increasingly complex observational effects, NPE produces accurate and well-calibrated posterior approximations for both shear components in the presence of blended galaxies, spatially varying point spread functions, stars, and detector artifacts. These results demonstrate that NPE can be a viable shear estimation method in settings where all anticipated features and artifacts can be simulated, a requirement that will become increasingly feasible as simulation fidelity improves in the coming decades.

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arXiv2026

Small-scale Lyman alpha forest cosmology with PRIYA: Constraints from XQ100 and KODIAQ-SQUAD one-dimensional flux power spectra

We present a new cosmological analysis of the small-scale Lyman alpha forest 1D flux power spectrum (P1D) using high-resolution quasar spectra from XQ100 and KODIAQ-SQUAD, interpreted through the PRIYA emulator. PRIYA is a suite of galaxy formation simulations spanning a range of cosmological and inhomogeneous HeII reionization parameters, enabling few-percent-level predictions of the P1D. These datasets, probing down to $k \sim 6\,h\,\mathrm{Mpc}^{-1}$ at $z = 2-5$, offer access to non-linear scales inaccessible to large-volume surveys like eBOSS. We find that the XQ100 P1D yields constraints on the primordial power spectrum parameters $(A_P, n_P)$ at pivot scale $k_0 = 0.78\,\mathrm{Mpc}^{-1}$ that are consistent with PRIYA results from eBOSS DR14 and Planck CMB, albeit with broader uncertainties. Notably, this is achieved without external IGM temperature data, showing that XQ100 alone provides stronger constraints on thermal history than eBOSS DR14. In contrast, the KODIAQ-SQUAD P1D favors a significantly higher $A_P$ value, driven by the selection bias toward high-column density absorbers (HCDs). We also find that the P1D at $k > 0.045\,\mathrm{s/km}$ is more sensitive to Lyman limit system contamination and thermal history. When imposing a prior on $(A_P, n_P)$, the reduced $χ^2$ remains unchanged and the inferred mean IGM temperature is unaffected, suggesting that cosmological and thermal parameters are largely sensitive to different scales. The XQ100 P1D therefore provides complementary information on thermal nuisance parameters, which can be jointly fit with eBOSS or DESI P1D measurements to improve cosmological constraints.

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arXiv2026

The Quasar Proximity Effect as an Alternative Probe of Quasar Pair Distances

Recently discovered quasar pairs at high redshifts ($z\gtrsim$5) are likely precursors to supermassive black hole mergers, providing a promising window to high redshift quasar growth mechanisms. However, the large uncertainties on their relative distances along the line-of-sight ($d_{\rm l.o.s.}$) limits our ability to characterize quasar pairs. In this study, we explore synthetic quasar proximity zone spectra as an alternative method to constrain the line-of-sight distance of quasar pairs. We find that for small sky-plane separations ($d_{\rm sky}\approx 10-100$ pkpc), a simple peak finding algorithm can easily distinguish between scenarios of $d_{\rm l.o.s.} \lesssim1$ pMpc and $\gtrsim1$ pMpc. For cases where the true $d_{\rm l.o.s.} \geq 3$ pMpc, the accuracy of $d_{\rm l.o.s.}$ estimation is $\approx 0.2$ pMpc. Large sky-plane separations of $d_{\rm sky}=1$ pMpc have larger absolute uncertainties in $d_{\rm l.o.s.}$ estimates, but the method can still easily distinguish between scenarios where $d_{\rm l.o.s.}\lesssim4$ pMpc and $\gtrsim4$ pMpc. $d_{\rm l.o.s.}$ estimates have an uncertainty of $\approx$0.5 pMpc when true $d_{\rm l.o.s.} \gtrsim4$ pMpc. Our proof-of-concept study illustrates the potential use of quasar proximity zones to constrain the 3-dimensional quasar pair configuration, providing an avenue to characterize quasar pairs.

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arXiv2026

Simulation-Based Inference for Probabilistic Galaxy Detection and Deblending

Stage-IV dark energy wide-field surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe an unprecedented number density of galaxies. As a result, the majority of imaged galaxies will visually overlap, a phenomenon known as blending. Blending is expected to be a leading source of systematic error in astronomical measurements. To mitigate this systematic, we propose a new probabilistic method for detecting, deblending, and measuring the properties of galaxies, called the Bayesian Light Source Separator (BLISS). Given an astronomical survey image, BLISS uses convolutional neural networks to produce a probabilistic astronomical catalog by approximating the posterior distribution over the number of light sources, their centroids' locations, and their types (galaxy vs. star). BLISS additionally includes a denoising autoencoder to reconstruct unblended galaxy profiles. As a first step towards demonstrating the feasibility of BLISS for cosmological applications, we apply our method to simulated single-band images whose properties are representative of year-10 LSST coadds. First, we study each BLISS component independently and examine its probabilistic output as a function of SNR and degree of blending. Then, by propagating the probabilistic detections from BLISS to its deblender, we produce per-object flux posteriors. Using these posteriors yields a substantial improvement in aperture flux residuals relative to deterministic detections alone, particularly for highly blended and faint objects. These results highlight the potential of BLISS as a scalable, uncertainty-aware tool for mitigating blending-induced systematics in next-generation cosmological surveys.

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arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

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arXiv2025

Neural Posterior Estimation for Cataloging Astronomical Images from the Legacy Survey of Space and Time

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will commence full-scale operations in 2026, yielding an unprecedented volume of astronomical images. Constructing an astronomical catalog, a table of imaged stars, galaxies, and their properties, is a fundamental step in most scientific workflows based on astronomical image data. Traditional deterministic cataloging methods lack statistical coherence as cataloging is an ill-posed problem, while existing probabilistic approaches suffer from computational inefficiency, inaccuracy, or the inability to perform inference with multiband coadded images, the primary output format for LSST images. In this article, we explore a recently developed Bayesian inference method called neural posterior estimation (NPE) as an approach to cataloging. NPE leverages deep learning to achieve both computational efficiency and high accuracy. When evaluated on the DC2 Simulated Sky Survey -- a highly realistic synthetic dataset designed to mimic LSST data -- NPE systematically outperforms the standard LSST pipeline in light source detection, flux measurement, star/galaxy classification, and galaxy shape measurement. Additionally, NPE provides well-calibrated posterior approximations. These promising results, obtained using simulated data, illustrate the potential of NPE in the absence of model misspecification. Although some degree of model misspecification is inevitable in the application of NPE to real LSST images, there are a variety of strategies to mitigate its effects.

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arXiv2025

Determining the Hubble Constant through Cross-Correlation of Galaxies and Gravitational Waves

Gravitational wave (GW) standard sirens have the potential to measure the Hubble constant $H_0$ in the local universe independently of the distance ladder, and thus offer unique new insights into the Hubble tension. A key challenge with standard sirens is detecting their electromagnetic counterparts, and therefore assigning redshifts to the measured distances. One promising way to proceed is to utilize GW `dark sirens' -- events without an identified electromagnetic counterpart -- and cross-correlate their angular distribution with that of galaxies. We present a quantitative study of how precisely the Hubble constant can be measured using tomographic cross-correlation between galaxies and GW sources. Overall, we find that the constraints on $H_0$ will be limited by the quality and quantity of GW data. We find that percent-level constraints on $H_0$ will primarily depend on achieving small distance uncertainties ($σ_{d_L}=0.1\,d_L$), obtaining a large number of GW dark sirens ($\gtrsim$$5{,}000$), and accurate sky localization in the tomographic analysis.

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arXiv2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images have spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a method of performing NPE with spatially varying backgrounds and PSFs. In this method, we generate synthetic catalogs and semi-synthetic images for these catalogs using randomly sampled PSF and background estimates from existing surveys. Using this data, we train a neural network, which takes an astronomical image and representations of its background and PSF as input, to output a probabilistic catalog. Our experiments with Sloan Digital Sky Survey data demonstrate the effectiveness of NPE in the presence of spatially varying backgrounds and PSFs for light source detection, star/galaxy separation, and flux measurement.

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arXiv2025

On the Physical Nature of Ly$α$ Transmission Spikes in High Redshift Quasar Spectra

We investigate Lyman-alpha (Ly$α$) transmission spikes at $5.2 < z < 6.8$ using synthetic quasar spectra from the ``Cosmic Reionization On Computers" simulations. We focus on understanding the relationship between these spikes and the properties of the intergalactic medium (IGM). Disentangling the complex interplay between IGM physics and the influence of galaxies on the generation of these spikes presents a significant challenge. To address this, we employ Explainable Boosting Machines, an interpretable machine learning algorithm, to quantify the relative impact of various IGM properties on the Ly$α$ flux. Our findings reveal that gas density is the primary factor influencing absorption strength, followed by the intensity of background radiation and the temperature of the IGM. Ionizing radiation from local sources (i.e. galaxies) appears to have a minimal effect on Ly$α$ flux. The simulations show that transmission spikes predominantly occur in regions of low gas density. Our results challenge recent observational studies suggesting the origin of these spikes in regions with enhanced radiation. We demonstrate that Ly$α$ transmission spikes are largely a product of the large-scale structure, of which galaxies are biased tracers.

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arXiv2025

Cosmic Reionization On Computers: Biases and Uncertainties in the Measured Mean Free Path at the End Stage of Reionization

Recent observations and analyses of absorption in quasar spectra suggest a rapid drop in the mean free path (MFP) at the late stage of reionization at $z\sim6$. We use the Cosmic Reionization on Computers simulation to examine potential biases in observed measurements of the MFP at the late stage of reionization, particularly in the presence of a quasar. We analyze three snapshots surrounding the `ankle' point of reionization history, when extended neutral patches of the intergalactic medium disappeared in the simulation box. Specifically, these are $z=6.8$ (true MFP $\approx 0.4$ pMpc), in addition to $z=6.1$ (true MFP $\approx 2$ pMpc) and $z=5.4$ (true MFP $\approx 6$ pMpc). We compare the inferred MFP $λ_{\rm mfp}$ from synthetic spectra fits to the true MFP. We find that the mean Lyman continuum (LyC) profile at $z=6.8$ changes significantly with quasar lifetime $t_Q$. We attribute this sensitivity to $t_Q$ to a combination of extended neutral IGM patches and the prevalence of small-scale dense clumps. Consequently, the inferred MFP can be biased by a factor of few depending on $t_Q$. On the other hand, for the $z=6.1$ and $z=5.4$ snapshots, the mean LyC profile shows minimal sensitivity to variation in $t_Q\gtrsim 1$ Myr. The inferred MFP in these two cases is accurate to the $\lesssim 30\%$ level. Our results highlight how modeling systematics can affect the inferred MFP, particularly in the regime of small true MFP ($\lesssim 0.5$ pMpc). We also discuss the potential of this regime to provide a testing ground for constraining quasar lifetimes from LyC profiles.

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arXiv2025

On the minimum number of radiation field parameters to specify gas cooling and heating functions

Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions computed by Cloudy in the presence of a generalized incident local radiation field. We characterize the radiation field through binned radiation field intensities instead of the photoionization rates used in our previous work. We find a set of 6 energy bins whose intensities exhibit relatively low correlation. We use these bins as features to train machine learning models to predict Cloudy cooling and heating functions at fixed metallicity. We compare the relative SHapley Additive exPlanation (SHAP) value importance of the features. From the SHAP analysis, we identify a feature subset of 3 energy bins ($0.5-1, 1-4$, and $13-16 \, \mathrm{Ry}$) with the largest importance and train additional models on this subset. We compare the mean squared errors and distribution of errors on both the entire training data table and a randomly selected 20% test set withheld from model training. The machine learning models trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have comparable accuracy everywhere, with errors $\gtrsim 10$ times smaller than for the interpolation table of Gnedin and Hollon (2012). We conclude that 3 energy bins (or 3 analogous photoionization rates: molecular hydrogen photodissociation, neutral hydrogen HI, and fully ionized carbon CVI) are sufficient to characterize the dependence of the gas cooling and heating functions on our assumed incident radiation field model.

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arXiv2025

Weak lensing mass-richness relation of redMaPPer clusters in the LSST DESC DC2 simulations

Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, where clusters are detected through their richness, cluster-weak gravitational lensing has proven to be a powerful tool to constrain the cluster mass-richness relation. This work is conducted as part of the Dark Energy Science Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory, starting in 2026. Weak lensing-inferred cluster properties, such as mass, suffer from several sources of bias. We constrain the mass-richness relation of 3,600 clusters detected by the redMaPPer algorithm in the cosmoDC2 extra-galactic mock catalog of the LSST DESC DC2 simulation, covering 440 square degrees, using number count measurements and either stacked weak lensing profiles or mean cluster masses in intervals of richness ($20 < λ< 200$) and redshift ($0.2 < z < 1$). We find that, for an LSST-like source galaxy density, our constraints are robust to changes in the concentration-mass relation and dark matter density profile modeling choices, when source redshifts and shapes are perfectly known. We find that photometric redshift uncertainties can introduce bias at the 1$σ$ level, which can be mitigated by an overall correction factor, fitted jointly with the scaling parameters. We find that including positive shear-richness covariance in the fit shifts the results by up to 0.5$σ$. Our constraints also compare fairly to a fiducial mass-richness relation, obtained from matching cosmoDC2 halo masses to redMaPPer-detected cluster richnesses.

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arXiv2025

The Three Hundred Project: Modeling Baryon and Hot-Gas Fraction Evolution in Simulated Clusters

The baryon fraction of galaxy clusters is a powerful tool to inform on the cosmological parameters while the hot-gas fraction provides indications on the physics of the intracluster plasma and its interplay with the processes driving galaxy formation. Using cosmological hydrodynamical simulations from The Three Hundred collaboration of about 300 simulated massive galaxy clusters with median mass $M_{500}\approx7 \times 10^{14}$M$_{\odot}$ at $z=0$, we model the relations between total mass and either baryon fraction or the hot gas fractions at overdensities $Δ= 2500$, $500$, and $200$ with respect to the cosmic critical density, and their evolution from $z\sim 0$ to $z\sim 1.3$. We fit the simulation results for such scaling relations against three analytic forms (linear, quadratic, and logarithmic in a logarithmic plane) and three forms for the redshift dependence, considering as a variable both the inverse of cosmic scale factor, $(1+z)$, and the Hubble expansion rate, $E(z)$. We show that power-law dependencies on cluster mass poorly describe the investigated relations. A power-law fails to simultaneously capture the flattening of the total baryon and gas fractions at high masses, their drop at the low masses, and the transition between these two regimes. The other two functional forms provide a more accurate description of the curvature in mass scaling. The fractions measured within smaller radii exhibit a stronger evolution than those measured within larger radii. From the analysis of these simulations, we conclude that as long as we include systems in the mass range herein investigated, the baryon or gas fraction can be accurately related to the total mass through either a parabola or a logarithm in the logarithmic plane. The trends are common to all modern hydro simulations, although the amplitude of the drop at low masses might differ [Abridged].

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