arXiv subjects

Francisco Maion

Publications and source records attributed to Francisco Maion.

10 recordsLinked to original sources

arXiv2026

Where the Forest Goes Dark: Halo-centered Characterization of High Column Density Systems with IllustrisTNG

High column density systems of neutral hydrogen (HCDs) --- Lyman limit systems (LLSs), sub-damped and damped Ly$α$ absorbers (DLAs) --- contaminate both the 3D and the 1D statistics of the Ly$α$ forest. Most DLAs are masked out from the analyses, but cleaning algorithms are not perfect, and weaker LLSs are individually undetectable, so the residual contamination is absorbed into nuisance parameters that dilute the cosmological constraining power. Simulations provide the ideal setting to study this effect, allowing absorption features to be directly traced back to the gas producing them. We identify high-column-density structures on the gas cells of the TNG50 hydrodynamical simulation, deblend them in velocity space, and split every sightline into HCD-only and forest-only spectra. Applied to halos at $z \simeq 3$ out to 50 virial radii, it yields each class's covering fraction against impact parameter $b$ and halo mass. The impact parameter defines the type of absorption appearing in the spectrum: DLAs give way to sub-DLAs at $b \approx 0.11\,R_{200c}$, sub-DLAs to LLSs at $0.3$, and LLSs to the Ly$α$-forest at $0.5$, which covers $\simeq 78\%$ of sightlines at $R_{200c}$. In units of $R_{200c}$ the sequence is nearly mass-independent over three decades in mass, so the virial radius sets the scale of the neutral gas distribution. A single Voigt component recovers the column density of essentially every damped system, but not of LLSs, whose absorption features often arise from several separate contributions from gas structures along the sightline. Our detailed characterization of HCDs gives the first step to the construction of advanced techniques to directly forward-model their contribution to Ly$α$ spectra.

astro-ph.CO↗

arXiv2026

Evaluating the flexibility of the MillenniumTNG galaxy formation model with multi-zoom re-simulations

In this study we introduce a new simulation campaign designed to understand how parameters that control star-formation and AGN feedback processes in cosmological hydrodynamical simulations impact observables such as the galaxy stellar-mass function (GSMF) and the gas fractions in large dark matter halos. These simulations are zoom-ins to halos selected from the MillenniumTNG (MTNG) simulation, and are run employing a novel multi-zoom approach which simultaneously re-simulates several sub-regions of a given large volume at a higher resolution than the background, thus reducing computational cost and imbalances in parallelization. We measure the GSMF and gas-fractions in halos for each of the re-simulations, and train Gaussian-process emulators on these quantities. The resulting emulators predict the GSMF and gas-fractions in halos with $\sim0.1\,\mathrm{dex}$ and $\sim 10\%$ precision respectively. Using the emulators we can simultaneously fit recent measurements of both quantities, in particular the lower gas fractions now observed even for comparatively massive clusters. Interestingly, we find a combination of parameters of the MTNG galaxy formation model that provides a qualitatively good fit to both the measured GSMF and gas fractions. This combination of parameters differs from the fiducial one mainly by requiring that stellar-feedback is significantly less energetic, and that kinetic AGN feedback events are significantly more energetic and rare. This finding implies that the MTNG model can be consistent with scenarios of strong feedback that remove large amounts of gas from groups and clusters, albeit we caution that we have not extensively examined the effect of these new parameters on many quantities for which MTNG made successful predictions.

astro-ph.GA↗

arXiv2025

The effect of baryons on the positions and velocities of satellite galaxies in the MTNG simulation

Mock galaxy catalogues are often constructed from dark-matter-only simulations based on the galaxy-halo connection. Although modern mocks can reproduce galaxy clustering to some extent, the absence of baryons affects the spatial and kinematic distributions of galaxies in ways that remain insufficiently quantified. We compare the positions and velocities of satellite galaxies in the MTNG hydrodynamic simulation with those in its dark-matter-only counterpart, assessing how baryonic effects influence galaxy clustering and contrasting them with the impact of galaxy selection, i.e. the dependence of clustering on sample definition. Using merger trees from both runs, we track satellite subhaloes until they become centrals, allowing us to match systems even when their z=0 positions differ. We then compute positional and velocity offsets as functions of halo mass and distance from the halo centre, and use these to construct a subhalo catalogue from the dark-matter-only simulation that reproduces the galaxy distribution in the hydrodynamic run. Satellites in the hydrodynamic simulation lie 3-4% closer to halo centres than in the dark-matter-only case, with an offset that is nearly constant with halo mass and increases toward smaller radii. Satellite velocities are also systematically higher in the dark-matter-only run. At scales of 0.1 Mpc/h, these spatial and kinematic differences produce 10-20% variations in clustering amplitude -- corresponding to 1-3$σ$ assuming DESI-like errors -- though the impact decreases at larger scales. These baryonic effects are relevant for cosmological and lensing analyses and should be accounted for when building high-fidelity mocks. However, they remain smaller than the differences introduced by galaxy selection, which thus represents the dominant source of uncertainty when constructing mocks based on observable quantities.

astro-ph.CO↗

arXiv2025

Probabilistic Lagrangian bias estimators and the cumulant bias expansion

The spatial distribution of galaxies is a highly complex phenomenon currently impossible to predict deterministically. However, by using a statistical $\textit{bias}$ relation, it becomes possible to robustly model the average abundance of galaxies as a function of the underlying matter density field. Understanding the properties and parametric description of the bias relation is key to extract cosmological information from future galaxy surveys. Here, we contribute to this topic primarily in two ways: (1) We develop a new set of probabilistic estimators for bias parameters using the moments of the Lagrangian galaxy environment distribution. These estimators include spatial corrections at different orders to measure bias parameters independently of the damping scale. We report robust measurements of a variety of bias parameters for haloes, including the tidal bias and its dependence with spin at a fixed mass. (2) We propose an alternative formulation of the bias expansion in terms of "cumulant bias parameters" that describe the response of the logarithmic galaxy density to large-scale perturbations. We find that cumulant biases of haloes are consistent with zero at orders $n > 2$. This suggests that: (i) previously reported bias relations at order $n > 2$ are an artefact of the entangled basis of the canonical bias expansion; (ii) the convergence of the bias expansion may be improved by phrasing it in terms of cumulants; (iii) the bias function is very well approximated by a Gaussian -- an avenue which we explore in a companion paper.

astro-ph.CO↗

arXiv2024

Probabilistic Estimators of Lagrangian Shape Biases: Universal Relations and Physical Insights

The intrinsic alignment of galaxies is a key factor in modeling weak-lensing observations and can serve as a valuable signal for both cosmological and astrophysical studies. Modelling this signal requires understanding how galaxy shapes form, and their relations to the large-scale gravitational field -- typically encoded in the value of large-scale shape-bias parameters. In this article we contribute to this topic in three ways: (i) developing new estimators of Lagrangian shape-biases (ii) applying them to measure the shape-biases of dark-matter halos (iii) interpreting these measurements to gain insight on the process of halo-shape formation. We show that our estimators produce results consistent with previous literature, and that they possess advantages with respect to previous methods, namely that the measurement of each bias parameter is completely independent from the others, and that bias parameters can be defined for each individual object. We measure universal relations between shape-bias parameters and peak-significance, $ν$. This relation for the first-order shape-bias parameter is linear at high $ν$, and converges to zero at low $ν$, which we interpret as strong evidence against the proposed scenario according to which galaxy shapes arise due to post-formation interaction with the large-scale tidal-field. We anticipate our estimators to be very useful in analyzing hydrodynamical simulations to extract physical understandings of galaxy shape formation, as well as establishing priors on the values of intrinsic-alignment biases.

astro-ph.CO↗

arXiv2024

HYMALAIA: A Hybrid Lagrangian Model for Intrinsic Alignments

The intrinsic alignment of galaxies is an important ingredient for modelling weak-lensing measurements, and a potentially valuable cosmological and astrophysical signal. In this paper, we present HYMALAIA: a new model to predict the intrinsic alignments of biased tracers. HYMALAIA is based on a perturbative expansion of the statistics of the Lagrangian shapes of objects, which is then advected to Eulerian space using the fully non-linear displacement field obtained from $N$-body simulations. We demonstrate that HYMALAIA is capable of consistently describing monopole and quadrupole of halo shape-shape and matter-shape correlators, and that, without increasing the number of free parameters, it does so more accurately than other perturbatively inspired models such as the non-linear alignment (NLA) model and the tidal-alignment-tidal-torquing (TATT) model.

astro-ph.CO↗

arXiv2022

Suppressing variance in 21-cm signal simulations during reionization

Current best limits on the 21-cm signal during reionization are provided at large scales ($\gtrsim$100 Mpc). To model these scales, enormous simulation volumes are required which are computationally expensive. We find that the primary source of uncertainty at these large scales is sample variance, which decides the minimum size of simulations required to analyse current and upcoming observations. In large-scale structure simulations, the method of `fixing' the initial conditions (ICs) to exactly follow the initial power spectrum and `pairing' two simulations with exactly out-of-phase ICs has been shown to significantly reduce sample variance. Here we apply this `fixing and pairing' (F\&P) approach to reionization simulations whose clustering signal originates from both density fluctuations and reionization bubbles. Using a semi-numerical code, we show that with the traditional method, simulation boxes of $L\simeq 500$ (300) Mpc are required to model the large-scale clustering signal at $k$=0.1 Mpc$^{-1}$ with a precision of 5 (10) per cent. Using F\&P, the simulation boxes can be reduced by a factor of 2 to obtain the same precision level. We conclude that the computing costs can be reduced by at least a factor of 4 when using the F\&P approach.

astro-ph.CO↗

arXiv2022

Statistics of biased tracers in variance-suppressed simulations

Cosmological simulations play an increasingly important role in analysing the observed large-scale structure of the Universe. Recently, they have been particularly important in building hybrid models that combine a perturbative bias expansion with displacement fields extracted from N-body simulations to describe the clustering of biased tracers. Here, we show that simulations that employ a technique referred to as "Fixing-and-pairing" (F&P) can dramatically improve the statistical precision of such hybrid models. Specifically, by numerical and analytic means, we show that F&P simulations provide unbiased estimates for all statistics employed by hybrid models while reducing, by up to two orders of magnitude, their uncertainty on large scales. This roughly implies that an EUCLID-like survey could be analysed using simulations of 2Gpc a side -- a 20% of the survey volume. Our work establishes the robustness of F&P for current hybrid theoretical models for galaxy clustering, an important step towards achieving an optimal exploitation of large-scale structure measurements.

astro-ph.CO↗

arXiv2022

The Bacco Simulation Project: Bacco Hybrid Lagrangian Bias Expansion Model in Redshift Space

We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a 2nd-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological $N$-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an 8-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to nonlinear scales ($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the Universe.

astro-ph.CO↗

arXiv2022

Priors on Lagrangian bias parameters from galaxy formation modelling

We study the relations among the parameters of the hybrid Lagrangian bias expansion model, fitting biased auto and cross power spectra up to $k_{\rm max} = 0.7 \, h \, \mathrm{Mpc}^{-1}$. We consider $\sim 8000$ halo and galaxy samples, with different halo masses, redshifts, galaxy number densities, and varying the parameters of the galaxy formation model. Galaxy samples are obtained through state-of-the-art extended subhalo abundance matching techniques and include both stellar-mass and star-formation-rate selected galaxies. All of these synthetic galaxies samples are publicly available at https://bacco.dipc.org/galpk.html. We find that the hybrid Lagrangian bias model provides accurate fits to all of our halo and galaxy samples. The coevolution relations between galaxy bias parameters, although roughly compatible with those obtained for haloes, show systematic shifts and larger scatter. We explore possible sources of this difference in terms of dependence on halo occupation and assembly bias of each sample. The bias parameter relations displayed in this work can be used as a prior for future Bayesian analyses employing the hybrid Lagrangian bias expansion model.

astro-ph.CO↗