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Francisco-Shu Kitaura

Publications and source records attributed to Francisco-Shu Kitaura.

At least 19 recordsLinked to original sources

Primordial non-Gaussianity as a large-scale cosmic-web bias

Primordial non-Gaussianity is a unique probe of the physics of inflation, but its large-scale-structure signature is difficult to measure because it appears on the largest, most cosmic-variance-limited modes. The standard view of local-type primordial non-Gaussianity is isotropic: it induces a scale-dependent correction to tracer bias, $Δb(k)\propto f_{\rm NL}k^{-2}$, on large scales. Here we show that the same large-scale response can be reformulated as an anisotropic, nonlocal environmental-bias effect. Motivated by the spectral cosmic-web hierarchy of Kitaura and Sinigaglia, we define a Hessian field $\mathcal{H}_{ij}(\bf{x})\equiv\partial_i\partial_j\nabla^{-4}δ(\bf{x})$, whose trace obeys $\mathrm{Tr}\mathcal{H}=\nabla^{-2}δ$ and therefore carries the same $k^{-2}$ enhancement that appears in the Dalal et al. scale-dependent-bias term. Diagonalizing $\mathcal{H}_{ij}$ defines four large-scale web environments, analogous to knots, filaments, sheets, and voids, although operating on scales much larger than the conventional nonlinear cosmic web. We assign one weight to each environment and test the construction with the AbacusPNG suite of local-PNG $N$-body simulations, using $f_{\rm NL}=\{-100,-30,30,100\}$. We show that these four weights reproduce the large-scale $k^{-2}$ rise of the tracer bias. We further show that existing halo catalogs without PNG can be post-processed to imprint an effective $f_{\rm NL}$ signature. Beyond mock generation, the resulting cosmic-web response motivates an observational statistic based on changes in tracer occupation of large-scale environments relative to a matched Gaussian reference, complementary to the conventional low-$k$ power spectrum.

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Spectral Hierarchy of the Cosmic Web

We introduce a spectral hierarchy of cosmic-web classifications obtained by applying simple scale-weighting kernels to the density field before performing a standard eigenvalue-based web classification. This unifies and extends several widely used web definitions within a single framework: the familiar potential/tidal web (large-scale, nonlocal), a curvature-based web (more local, peak- and ridge-sensitive), and additional higher-derivative levels that progressively emphasize smaller-scale structure. Because the classification is built from second derivatives of the filtered field, successive hierarchy levels align naturally with operator families that appear in renormalised bias and effective descriptions of large-scale structure, providing an explicit bridge between cosmic-web environments and long- and short-range nonlocal bias ingredients. We quantify the information content of the hierarchy with a compact statistic: we map each cell to one of four ordered web types (void, sheet, filament, knot), construct a corresponding ``web contrast'' field, and measure its cross-correlation with halos from the AbacusSummit simulation suite on a coarse mesh with $ΔL\simeq 5.5\,h^{-1}\mathrm{Mpc}$. We find that the hierarchy retains significant tracer-relevant information from very large scales down to the mesh Nyquist limit, with the more local (curvature/higher-derivative) levels dominating toward nonlinear scales. This makes the spectral hierarchy a practical, interpretable conditioning basis for fast mock-galaxy production and field-level modelling, and a flexible tool for studying environment-dependent clustering and assembly bias.

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Ridged Lagrangian Perturbation Theory (RLPT)

Galaxy surveys demand fast large-scale structure forward models that preserve large-scale phases while providing realistic nonlinear morphology at fixed force resolution. Single-step Lagrangian Perturbation Theory (LPT) solvers are efficient, but they typically yield overly diffuse filaments and knots and underpredict small-scale clustering. We introduce Ridged Lagrangian Perturbation Theory (RLPT), a modular two-step scheme: a standard long-range LPT/ALPT transport is followed by a single post-processing Eulerian ridging update that reconstructs a short-range, curl-free displacement from the realised density field through a smooth scale separation and a Poisson inversion. This explicit completion layer is inexpensive, preserves the large-scale solution, and provides a small set of transparent parameters to tune the short-range response. We test RLPT against particle-mesh and $N$-body references and find that one additional ridging step systematically improves both nonlinear power and field-level agreement relative to 2LPT/ALPT baselines. Finally, we demonstrate that ridging can be repurposed as a deterministic subgrid relocation model: even when the underlying dark-matter field is only "good enough" on the mesh, ridging enables controlled tuning of tracer clustering beyond the nominal resolution, which is particularly relevant for mock-galaxy production and observational systematics sensitive to close pairs.

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Emergence of Complex Web Structures

Complex structures often emerge from initially homogeneous or weakly correlated states. We address the apparent tension between this ordering and entropy growth through a unified framework combining semi-microscopic phase-space dynamics, transport geometry, information theory, and coarse-grained effective modeling. The key point is that entropy depends on the level of description: a coarse-grained spatial field may become more ordered as structure forms, even while the full phase-space description becomes more complex through shell crossing, multistreaming, and the activation of velocity degrees of freedom. Using a Lagrangian--Eulerian transport map, we show how density amplification is governed by the Jacobian of the deformation and how anisotropic collapse arises from the eigenvalues of a hierarchy of deformation tensors. Long-range interaction or information flow is encoded in the displacement field, so that nonlocality enters directly through transport. We connect this geometric description to a maximum-entropy Gaussian baseline and show how nonlinear transport and nonlocal coupling generate scale coupling, higher-order correlations, and non-Gaussianity. We then formulate a Landau--Ginzburg description in which the growth of seed anisotropies is interpreted as the activation of lower effective free-energy branches, providing a coarse-grained realization of self-organization. Applied to generated cosmological fields, this framework indicates that the nonlocal tidal level becomes relevant already at moderate overdensity. Although cosmological structure formation is the main realization considered here, the framework is intended more broadly as a mesoscopic language for systems in which transport, anisotropy, nonlocality, and self-organization are central.

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Cosmic variance or galaxy bias? Disentangling finite-volume and galaxy formation effects in cosmological analysis

Current and forthcoming galaxy redshift surveys, such as DESI and Euclid, are going to bring cosmological analysis to an unprecedentedly exquisite level of precision in the determination of the cosmological parameters. However, these efforts require a high degree of control over theory and systematics, to obtain unbiased results. In this sense, the cosmic variance associated to finite-volume effects represents a major challenge and should adequately accounted for. In this work, we revisit the definition of cosmic variance and develop a novel framework to describe it using a `galaxy biasing' formalism. In particular, we use halo/galaxy Eulerian perturbation theory to relate the density field from an arbitrary cosmic realization to its counterpart having statistical properties reproducing the ensemble average, introducing a new set of bias parameters. We then apply this idea to the description of the non-linear shift of BAO, disentangling the source of uncertainty from cosmic variance and galaxy biasing associated with the measurement of the BAO scale. We finally check our analytical argument against cosmological variance-suppressed $N$-body simulations, finding an expected reduction in the uncertainty on the BAO peak position. We conclude that extra care should be used when inferring cosmological information from perturbative approaches involving the estimation of bias parameters and propose new practical strategies to optimally leverage the novel formulation of cosmic variance presented herein in cosmological analysis.

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HI Simulations for Cosmology with the SKA Observatory

We present a comparative overview of state-of-the-art methods for modelling the distribution of neutral hydrogen (HI) in the post-reionization Universe, developed in preparation for upcoming SKAO cosmological surveys. Our aim is to assess how different physical and empirical assumptions reflect into predictions for key observables such as the cosmic HI density, the HI mass function, and the HI-halo mass relation. We consider both: (i) semi-analytical approaches that self-consistently evolve baryonic components within dark matter merger trees through physically motivated prescriptions and (ii) empirical schemes tailored to different observables and based on fast approximations designed for large ensemble studies. By comparing the predictions from the different methods considered, we find overall consistency in integrated quantities such as $Ω_{\mathrm{HI}}$, yet systematic differences in the detailed shape and scatter of the HI--halo mass relation and its redshift evolution. Semi-analytical models offer physically grounded predictions but depend on assumed prescriptions, while empirical methods provide flexibility and computational efficiency at the expense of robustness in extrapolated regions of the parameter space. The increasing number of HI measurements from SKA precursors and pathfinders (including surveys with MeerKAT, ASKAP, and FAST) will provide critical observational constraints to refine and calibrate current simulation methodologies. In turn, increasingly realistic HI simulations play a key role in interpreting these data, guiding survey design and analysis strategies, in preparation for the advent of SKAO data.

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ELG$\times$LRG distribution through dark matter halo dynamics

We investigate the clustering and halo occupation distribution (HOD) of DESI Y1 emission-line (ELGs) and luminous red (LRGs) galaxies at $0.8<z<1.1$, including their cross-correlation (ELG$\times$LRG), using the AbacusSummit suite and a new Halo Occupation Model (HOMe) for galaxy multi-tracers. This integrates intra-halo dynamics, halo exclusion, and quenching, bridging insights from hydrodynamical, HOD, abundance-matching, and semi-analytic studies. Leveraging full phase-space information from the Uchuu N-body simulation, and sampling satellites from dark-matter particle positions via physically motivated prescriptions, HOMe reproduces the anisotropic clustering down to $s=200\,h^{-1}$kpc with unprecedented accuracy. Model parameters are inferred solely from two-point statistics using a two-level Bayesian framework, yielding high-fidelity ELG, LRG and cross-reference catalogs. We find that satellite ELGs behave as incoherent flows within their parent halos, dominating the clustering below $4\,h^{-1}$Mpc. The HOD from the best-fit HOMe has the following properties: (i) 90.50% (85.91%) of ELGs (LRGs) are central galaxies without satellites, residing in halos of $M_{\rm vir}\sim6.6\times10^{11}\,(1.2\times10^{13})\,h^{-1}{\rm M}_\odot$; (ii) the ELG$\times$LRG cross-correlation is governed by central-central pairs and shaped by halo exclusion on $2-5\,h^{-1}$Mpc scales; (iii) 9.50% (14.09%) of ELGs (LRGs) are satellites, of which 1.09% (3.52%) inhabit halos with a central galaxy of the same species in a maximally conformal configuration, 7.02% (0.005%) orbit complementary hosts in a minimally conformal state, and 0.58% (10.57%) are orphans. The high sensitivity of HOMe precisely captures the dynamics of satellites in different host environments, opening a promising avenue for understanding systematics, the dynamical nature of dark matter, potentially distinguishing gravity models.

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Fast and accurate Gaia-unWISE quasar mock catalogs from LPT and Eulerian bias

We present $100$ full-sky quasar spectrophotometric mock catalogs with smooth redshift evolution from $z=0$ to $z\sim 4$, tailored to analyze the Gaia-unWISE Quasar Catalog (Quaia). In particular, we apply a novel hierarchical nonlocal nonlinear bias scheme (Hicobian) to dark matter fields generated through Augmented Lagrangian Perturbation Theory on the lightcone (WebON code), calibrating the free parameters of the bias model on Abacus quasar HOD mock catalogs tuned to reproduce DESI Early Data Release observations in real and redshift space. After having obtained such accurate spectroscopic catalogs, we inject in the mocks the observational effects characterizing the Quaia catalog: (i) spectrophotometric redshift uncertainties, (ii) the angular selection function, and (iii) the redshift number counts distribution. We assess the accuracy of our catalogs by validating a number of summary statistics: the full-sky QSO maps, the redshift uncertainty distributions as a function of redshift, the redshift $n(z)$ distribution, the angular power spectra and their normalized covariance matrices, and the angular two-point correlation functions. We find excellent agreement between these metrics from the mocks and from the Quaia catalog. We publicly release the mock catalogs to the community.

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Tracing the high-z cosmic web with Quaia: catalogues of voids and clusters in the quasar distribution

Understanding the formation and evolution of the cosmic web of galaxies is a fundamental goal of cosmology, using various tracers of the cosmic large-scale structure at an ever wider range of redshifts. Our principal aim is to advance the mapping of the cosmic web at high redshifts using observational and synthetic catalogues of quasars (QSOs), which offer a powerful probe of structure formation and the validity of the concordance cosmological model. In this analysis, we selected 708,483 QSOs at $0.8<z<2.2$ from the Quaia data set, allowing a reconstruction of the matter density field using 24,372 deg$^2$ sky area with a well-understood selection function, and thus going beyond previous studies. Using the REVOLVER method, we created catalogues of voids and clusters based on the estimation of the local density at QSO positions with Voronoi tessellation. We tested the consistency of Quaia data and 50 mock catalogues, including various parameters of the voids and clusters in data subsets, and also measurements of the density profiles of these cosmic super-structures at $100 h^{-1}$Mpc scales. We identified 12,842 voids and 41,111 clusters in the distribution of Quaia QSOs. The agreement between data and mocks is at a level of 5-10%, considering void and cluster radii, average inner density, and density profiles. In particular, we tested the role of survey mask proximity effects in the void and cluster detection, which albeit present, are consistent in simulations and observations. The largest voids and clusters reach $R_{eff} \approx 250 h^{-1}$Mpc and $150 h^{-1}$Mpc, respectively, but without evidence for ultra-large cosmic structures exceeding the dimensions of the largest structures in the mocks. As an important deliverable, we share our density field estimation, void catalogues, and cluster catalogues with the public, allowing various additional cross-correlation probes at high-z.

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Field-level Lyman-alpha forest modelling in redshift space via augmented non-local Fluctuating Gunn-Peterson Approximation

We present an improved analytical model to predict the Lyman-alpha forest at the field level in redshift space from the dark matter field, expanding upon the widely-used Fluctuating Gunn-Peterson approximation (FGPA). In particular, we introduce the dependence on the cosmic web environment (knots, filaments, sheets, voids) in the model, thereby effectively accounting for non-local bias. Furthermore, we include a detailed treatment of velocity bias in the redshift space distortions modelling, allowing the velocity bias to be cosmic-web dependent. We find evidence for a significant difference of the same model parameters in different environments, suggesting that for the investigated setup the simple standard FGPA is not able to adequately predict the Lyman-alpha forest in the different cosmic web regimes. We reproduce the summary statistics of the reference cosmological hydrodynamic simulation we use for comparison, yielding accurate mean transmitted flux, probability distribution function, 3D power spectrum, and bispectrum. In particular, we achieve maximum deviation and average deviations accuracy in the Lyman-alpha forest 3D power spectrum of $\sim 3\%$ and $\sim 0.1\%$ up to $k\sim 0.4 \, h \, {\rm Mpc}^{-1}$, $\sim 5\%$ and $\sim 1.8\%$ up to $k \sim 1.4 \, h \, {\rm Mpc}^{-1}$. Our new model outperforms previous analytical efforts to predict the Lyman-alpha forest at the field level in all the probed summary statistics, and has the potential to become instrumental in the generation of fast accurate mocks for covariance matrices estimation in the context of current and forthcoming Lyman-alpha forest surveys.

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The negative BAO shift in the Ly$α$ forest from cosmological simulations

We present the first measurement of the Ly$α$ forest BAO shift parameter from cosmological simulations. In particular, we generate a suite of $1000$ accurate effective field-level bias-based Ly$α$ forest simulations of volume $V=(1 \, h^{-1} \, {\rm Gpc})^3$ at $z=2$, both in real and redshift space, calibrated upon two fixed-and-paired cosmological hydrodynamic simulations. To measure the BAO, we stack the three-dimensional power spectra of the $1000$ different realizations, compute the average, and use a model accounting for a proper smooth-peak component decomposition of the power spectrum, to fit it via an efficient Markov Chain Monte Carlo scheme estimating the covariance matrices directly from the simulations. We report the BAO shift parameters to be $α=0.9969^{+0.0014}_{-0.0014}$ and $α=0.9905^{+0.0027}_{-0.0027}$ in real and redshift space, respectively. We also measure the bias $b_{\rm lya}$ and the BAO broadening parameter $Σ_{\rm nl}$, finding $b_{\rm lya}=-0.1786^{+0.0001}_{-0.0001}$ and $Σ_{\rm nl}=3.87^{+0.20}_{-0.20}$ in real space, and $b_{\rm lya}=-0.073^{+0.005}_{-0.004}$ and $Σ_{\rm nl}=6.55^{+0.23}_{-0.22}$ in redshift space. Moreover, we measure the linear Kaiser factor $β_{\rm lya}=1.39^{+0.24}_{-0.18}$ from the isotropic redshift space fit. Overall, we find evidence for a negative shift of the BAO peak at the $\sim 2.2σ$ and $\sim 3.5σ$ level in real and redshift space, respectively. This work sets new important theoretical constraints on the Ly$α$ forest BAO scale and offers a potential solution to the tension emerging from previous observational analysis, in light of ongoing and upcoming Ly$α$ forest spectroscopic surveys, such as DESI, PFS, and WEAVE-QSO.

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Fast simulation mapping: from standard to modified gravity cosmologies using the bias assignment method

We assess the effectiveness of a non-parametric bias model in generating mock halo catalogues for modified gravity (MG) cosmologies, relying on the distribution of dark matter from either MG or $Λ$CDM. We aim to generate halo catalogues that effectively capture the distinct impact of MG, ensuring high accuracy in both two- and three-point statistics for comprehensive analysis of large-scale structures. As part of this study we aim at investigating the inclusion of MG into non-local bias to directly map the tracers onto $Λ$CDM fields, which would save many computational costs. We employ the bias assignment method (BAM) to model halo distribution statistics by leveraging seven high-resolution COLA simulations of MG cosmologies. Taking into account cosmic-web dependencies when learning the bias relations, we design two experiments to map the MG effects: one utilising the consistent MG density fields and the other employing the benchmark $Λ$CDM density field. BAM generates MG halo catalogues from both calibrations experiments excelling in summary statistics, achieving a $\sim 1\%$ accuracy in the power spectrum across a wide range of $k$-modes, with only minimal differences well below 10\% at modes subject to cosmic variance, particularly below $k<0.07$ $h$Mpc$^{-1}$. The reduced bispectrum remains consistent with the reference catalogues within 10\% for the studied configuration. Our results demonstrate that a non-linear and non-local bias description can model the effects of MG starting from a $Λ$CDM dark matter field.

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CosmoMIA: Cosmic Web-based redshift space halo distribution

Modern galaxy surveys demand extensive survey volumes and resolutions surpassing current dark matter-only simulations' capabilities. To address this, many methods employ effective bias models on the dark matter field to approximate object counts on a grid. However, realistic catalogs necessitate specific coordinates and velocities for a comprehensive understanding of the Universe. In this research, we explore sub-grid modeling to create accurate catalogs, beginning with coarse grid number counts at resolutions of approximately $5.5\,h^{-1}\rm Mpc$ per side. These resolutions strike a balance between modeling nonlinear damping of baryon acoustic oscillations and facilitating large-volume simulations. Augmented Lagrangian Perturbation Theory (ALPT) is utilized to model the dark matter field and motions, replicating the clustering of a halo catalog derived from a massive simulation at $z=1.1$. Our approach involves four key stages: Tracer Assignment: Allocating dark matter particles to tracers based on grid cell counts, generating additional particles to address discrepancies. Attractor Identification: Defining attractors based on particle cosmic web environments, acting as gravitational focal points. Tracer Collapse: Guiding tracers towards attractors, simulating structure collapse. Redshift Space Distortions: Introducing redshift space distortions to simulated catalogs using ALPT and a random dispersion term. Results demonstrate accurate reproduction of monopoles and quadrupoles up to wave numbers of approximately $k=0.6\,h$ Mpc$^{-1}$. This method holds significant promise for galaxy surveys like DESI, EUCLID, and LSST, enhancing our understanding of the cosmos across scales.

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Bayesian deep learning for cosmic volumes with modified gravity

The new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. A robust cosmological analysis of the large-scale structure demands exploiting the nonlinear information encoded in the cosmic web. Machine Learning techniques provide such tools, however, do not provide a priori assessment of uncertainties. This study aims at extracting cosmological parameters from modified gravity (MG) simulations through deep neural networks endowed with uncertainty estimations. We implement Bayesian neural networks (BNNs) with an enriched approximate posterior distribution considering two cases: one with a single Bayesian last layer (BLL), and another one with Bayesian layers at all levels (FullB). We train both BNNs with real-space density fields and power-spectra from a suite of 2000 dark matter only particle mesh $N$-body simulations including modified gravity models relying on MG-PICOLA covering 256 $h^{-1}$ Mpc side cubical volumes with 128$^3$ particles. BNNs excel in accurately predicting parameters for $Ω_m$ and $σ_8$ and their respective correlation with the MG parameter. We find out that BNNs yield well-calibrated uncertainty estimates overcoming the over- and under-estimation issues in traditional neural networks. We observe that the presence of MG parameter leads to a significant degeneracy with $σ_8$ being one of the possible explanations of the poor MG predictions. Ignoring MG, we obtain a deviation of the relative errors in $Ω_m$ and $σ_8$ by at least $30\%$. Moreover, we report consistent results from the density field and power spectra analysis, and comparable results between BLL and FullB experiments which permits us to save computing time by a factor of two. This work contributes in setting the path to extract cosmological parameters from complete small cosmic volumes towards the highly nonlinear regime.

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DESI Mock Challenge: Halo and galaxy catalogs with the bias assignment method

We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). We use the bias assignment method (BAM) to model the statistics of halo distribution through a learning algorithm using a few detailed $N$-body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. Using specific models of halo occupation distributions, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the DESI experiment. BAM generates mock catalogues with per cent accuracy in a number of summary statistics, such as the abundance, the two- and three-point statistics of halo distributions, both in real and redshift space. In particular, the mock galaxy catalogues display $\sim 3\%-10\%$ accuracy in the multipoles of the power spectrum up to scales of $k\sim 0.4\,h^{-1}{\rm Mpc}$. We show that covariance matrices of two- and three-point statistics obtained with BAM display a similar structure to the reference simulation. BAM offers an efficient way to produce mock halo catalogues with accurate two- and three-point statistics, and is able to generate a variety of multi-tracer catalogues with precise covariance matrices of several cosmological probes. We discuss future developments of the algorithm towards mock production in DESI and other galaxy-redshift surveys. (Abridged)

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Covariance matrices for variance-suppressed simulations

Cosmological $N$-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistical uncertainties in simulations, usually achieved by increasing simulation sizes at the expense of computational power. It was recently proposed to reduce simulation variance without incurring additional computational costs by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data, but requires us to revisit current methods for estimating covariance matrices of clustering statistics for simulations. In this work, we find that it is not trivial to construct covariance matrices analytically for fixed-amplitude simulations, but we demonstrate that EZmock (Effective Zel'dovich approximation mock catalogue), the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for such simulations. We further examine how the variance suppression obtained by amplitude-fixing depends on three-point clustering, small-scale clustering, and galaxy bias, and propose intuitive explanations for the effects we observe based on the EZmock bias model.

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Cosmic void exclusion models and their impact on the distance scale measurements from large scale structure

Baryonic Acoustic Oscillations (BAOs) studies based on the clustering of voids and matter tracers provide important constraints on cosmological parameters related to the expansion of the Universe. However, modelling the void exclusion effect is an important challenge for fully exploiting the potential of this kind of analyses. We thus develop two numerical methods to describe the clustering of cosmic voids. Neither model requires additional cosmological information beyond that assumed within the galaxy de-wiggled model. The models consist in power spectra whose performance we assess in comparison to a parabolic model on Patchy cubic and light-cone mocks. Moreover, we test their robustness against systematic effects and the reconstruction technique. The void model power spectra and the parabolic model with a fixed parameter provide strongly correlated values for the Alcock-Paczynski ($α$) parameter, for boxes and light-cones likewise. The resulting $α$ values -- for all three models -- are unbiased and their uncertainties are correctly estimated. However, the numerical models show less variation with the fitting range compared to the parabolic one. The Bayesian evidence suggests that the numerical techniques are often favoured compared to the parabolic model. Moreover, the void model power spectra computed on boxes can describe the void clustering from light-cones as well as from boxes. The same void model power spectra can be used for the study of pre- and post-reconstructed data-sets. Lastly, the two numerical techniques are resilient against the studied systematic effects. Consequently, using either of the two new void models, one can more robustly measure cosmological parameters.

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Predicted future fate of COSMOS galaxy protoclusters over 11 Gyr with constrained simulations

Cosmological simulations are crucial tools in studying the Universe, but they typically do not directly match real observed structures. Constrained cosmological simulations, on the other hand, are designed to match the observed distribution of galaxies. Here we present constrained simulations based on spectroscopic surveys at a redshift of z~2.3, corresponding to an epoch of nearly 11 Gyrs ago. This allows us to 'fast-forward' the simulation to our present-day and study the evolution of observed cosmic structures self-consistently. We confirm that several previously-reported protoclusters will evolve into massive galaxy clusters by our present epoch, including the 'Hyperion' structure that we predict will collapse into a giant filamentary supercluster spanning 100 Megaparsecs. We also discover previously unknown protoclusters, with lower final masses than typically detectable by other methods, that nearly double the number of known protoclusters within this volume. Constrained simulations, applied to future high-redshift datasets, represents a unique opportunity for studying early structure formation and matching galaxy properties between high and low redshifts.

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