arXiv subjects

Emiliano Sefusatti

Publications and source records attributed to Emiliano Sefusatti.

At least 19 recordsLinked to original sources

arXiv2026

Constraints on Ultra-Light Axions from the DESI DR1 Full Shape, Planck and ACT

We present updated bounds on ultra-light axions (ULAs) as a subcomponent of dark matter, derived from the full-shape analysis of the DESI Data Release 1 galaxy power spectra combined with Cosmic Microwave Background (CMB) data from ACT and Planck. We focus on the mass window $10^{-32}\,\mathrm{eV}\leq m_a \leq 10^{-24}\,\mathrm{eV}$, employing state-of-the-art analysis methods rooted in the Effective Field Theory of Large Scale Structure. For the smallest masses, our joint analysis with DESI improves over CMB-only constraints by more than a factor of 2, establishing the most stringent limits to date. For instance, for $m_a \sim 10^{-29}\,\mathrm{eV}$ ULAs are constrained to be a fraction as small as $0.3\%$ of the total matter energy density. The DESI Luminous Red Galaxy sample shows a mild preference for an ULA subcomponent with $m_a \approx 10^{-26}\,\mathrm{eV}$, mirroring previous hints from BOSS, but this preference vanishes upon combination with CMB data. Probing the largest masses, $m_a\gtrsim10^{-25}\,\mathrm{eV}$, in future studies will benefit from extending the data analysis to smaller scales, both for galaxy clustering and CMB lensing, but will also require concurrent improvements in the theoretical modeling.

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arXiv2026

Complex yet Hermitian: Gaussian covariance of cross-correlation and multi-tracer power spectra

Accurate modelling of the covariance of clustering observables is essential to fully exploit current and future survey data, which is expected to constrain large-scale clustering signals with unprecedented precision. Computational costs of simulation-based estimates motivate analytical approaches, especially in light of the growing interest towards multi-tracer analyses and parity-odd signatures in two-point statistics, which respectively mitigate cosmic variance and probe relativistic projection effects on cosmological scales. In this work, we generalise previous theoretical results for the Gaussian covariance of multi-tracer power spectrum measurements, providing a general expression applicable to both real (even-parity) and complex (both even- and odd-parity) power spectra. We focus on a generic weighted estimator at first, and then showcase how our general formalism applies to Legendre power spectrum multipoles and two-dimensional power spectrum, recovering known limits in appropriate cases. We validate our predictions against Gaussian Monte Carlo simulations and investigate the structure of the covariance matrix, including the Hermitian properties of its imaginary part.

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arXiv2026

On the impact of baryons on the halo mass function, bias, and cluster cosmology

Luminous matter produces very energetic events, such as active galactic nuclei and supernova explosions, that significantly affect the internal regions of galaxy clusters. Although the current uncertainty in the effect of baryonic physics on cluster statistics is subdominant as compared to other systematics, the picture is likely to change soon as the amount of high-quality data is growing fast, urging the community to keep theoretical systematic uncertainties below the ever-growing statistical precision. In this paper, we study the effect of baryons on galaxy clusters, and their impact on the cosmological applications of clusters, using the Magneticum suite of cosmological hydrodynamical simulations. We show that the impact of baryons on the halo mass function can be recast in terms on a variation of the mass of the halos simulated with pure N-body, when baryonic effects are included. The halo mass function and halo bias are only indirectly affected. Finally, we demonstrate that neglecting baryonic effects on halos mass function and bias would significantly alter the inference of cosmological parameters from high-sensitivity next-generations surveys of galaxy clusters.

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arXiv2026

The Effective Field Theory of Large Scale Structure for Mixed Dark Matter Scenarios

We initiate a systematic study of the perturbative nonlinear dynamics of cosmological fluctuations in dark sectors comprising a fraction of non-cold dark matter, for example ultra-light axions or light thermal relics. These mixed dark matter scenarios exhibit suppressed growth of perturbations below a characteristic, cosmologically relevant, scale associated with the microscopic nature of the non-cold species. As a consequence, the scale-free nonlinear solutions developed for pure cold dark matter and for massive neutrinos do not, in general, apply. We thus extend the Effective Field Theory of Large Scale Structure to model the coupled fluctuations of the cold and non-cold dark matter components, describing the latter as a perfect fluid with finite sound speed at linear level. We provide new analytical solutions wherever possible and devise an accurate and computationally tractable prescription for the evaluation of the one-loop galaxy power spectrum, which can be applied to probe mixed dark matter scenarios with current and upcoming galaxy survey data. As a first application of this framework, we derive updated constraints on the energy density in ultra-light axions using a combination of Planck and BOSS data. Our refined theoretical modeling leads to somewhat weaker bounds compared to previous analyses.

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arXiv2026

Probing General Relativity on Cosmological Scales in the 2040s

General relativity is exquisitely tested in strong-field regimes, yet its validity on cosmological scales remains largely unexplored. Upcoming wide and deep large-scale structure surveys will access the ultra-large, linear scales where relativistic effects - Doppler terms, gravitational redshift, lensing magnification, and potential evolution - leave significant imprints in the clustering of galaxies. These signatures represent unique probes of spacetime that are inaccessible to standard Newtonian analyses but increasingly important as survey volumes grow. We outline the scientific potential of next-generation facilities, such as those envisioned within ESO's Expanding Horizons programme, to deliver the first robust measurements of relativistic effects in large-scale structure through multi-tracer power spectra and the single-tracer bispectrum of high-redshift Lyman-break galaxies. Detecting these contributions would open a new window on gravity, enabling precision tests of general relativity and its alternatives on cosmological scales in the 2040s.

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arXiv2025

Redshift-Space Distortions in Massive Neutrinos Cosmologies

We study the Kaiser formula for biased tracers in massive neutrino cosmologies by comparing its predictions with a large set of N-body simulations. In particular, we examine the ambiguity in the definition of the peculiar velocity contribution at linear level, whether it should be expressed in terms of the total matter velocity field or only the cold-matter component, comprised of cold dark matter and baryons. We revisit and extend previous qualitative studies on the topic with larger statistics and including a full fit of halo power spectrum and halo-matter cross-power spectrum measurements. We find clear evidence that the clustering of halos in redshift space is correctly described assuming that halo velocity is tracing the velocity field of cold-matter. The opposite assumption provides a worse fit to the simulated data and can lead to a spurious running of nuisance parameters in the perturbative galaxy power spectrum model.

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arXiv2024

Bispectrum-window convolution via Hankel transform

We present a method to perform the exact convolution of the model prediction for bispectrum multipoles in redshift space with the survey window function. We extend a widely applied method for the power spectrum convolution to the bispectrum, taking advantage of a 2D-FFTlog algorithm. As a preliminary test of its accuracy, we consider the toy model of a spherical window function in real space. This setup provides an analytical evaluation of the 3-point function of the window, and therefore it allows to isolate and quantify possible systematic errors of the method. We find that our implementation of the convolution in terms of a mixing matrix shows differences at the percent level in comparison to the measurements from a very large set of mock halo catalogs. It is also able to recover unbiased constraints on halo bias parameters in a likelihood analysis of a set of numerical simulations with a total volume of $100\, h^{-3} \, {\rm Gpc}^3$. For the level of accuracy required by these tests, the multiplication with the mixing matrix is performed in the time of one second or less.

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arXiv2024

Bispectrum non-Gaussian Covariance in Redshift Space

We provide an analytical description of the galaxy bispectrum covariance and the power spectrum-bispectrum cross-covariance in redshift space that captures the dominant non-Gaussian contributions. The Gaussian prediction for the variance of the halo bispectrum monopole significantly underestimates numerical estimates particularly for squeezed triangles, that is bispectrum triangular configurations where one side is much smaller than the other two, whereas the effect is relatively less important when considering the quadrupole. We propose an expression for the missing non-Gaussian contribution valid in the squeezed limit that requires an accurate modeling of the bispectrum alone. We validate our model against the numerical covariance estimated from a large suite of mock catalogs and find that it accurately predicts the variance as well as the dominant off-diagonal terms. We also present an expression for the cross-covariance between power spectrum and bispectrum multipoles and likewise find it to provide a good description of the numerical results.

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arXiv2024

On the range of validity of perturbative models for galaxy clustering and its uncertainty

We explore the reach of analytical models at one-loop in Perturbation Theory (PT) to accurately describe measurements of the galaxy power spectrum from numerical simulations in redshift space. We consider the validity range in terms of three different diagnostics: 1) the goodness of fit; 2) a figure-of-bias quantifying the error in recovering the fiducial value of a cosmological parameter; 3) an internal consistency check of the theoretical model quantifying the running of the model parameters with the scale cut. We consider different sets of measurements corresponding to an increasing cumulative simulation volume in redshift space. For each volume we define a median value and the associated scatter for the largest wavenumber where the model is valid (the $k$-reach of the model). We find, as a rather general result, that the median value of the reach decreases with the simulation volume, as expected since the smaller statistical errors provide a more stringent test for the model. This is true for all the three definitions considered, with the one given in terms of the figure-of-bias providing the most stringent scale cut. More interestingly, we find as well that the error associated with the $k$-reach value is quite large, with a significant probability of being as low as 0.1$\, h \, {\rm Mpc}^{-1}$ (or, more generally, up to 40% smaller than the median) for all the simulation volumes considered. We explore as well the additional information on the growth rate parameter encoded in the power spectrum hexadecapole, compared to the analysis of monopole and quadrupole, as a function of simulation volume. While our analysis is, in many ways, rather simplified, we find that the gain in the determination of the growth rate is quite small in absolute value and well within the statistical error on the corresponding figure of merit.

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arXiv2022

Window function convolution with deep neural network models

Traditional estimators of the galaxy power spectrum and bispectrum are sensitive to the survey geometry. They yield spectra that differ from the true underlying signal since they are convolved with the window function of the survey. For the current and future generations of experiments, this bias is statistically significant on large scales. It is thus imperative that the effect of the window function on the summary statistics of the galaxy distribution is accurately modelled. Moreover, this operation must be computationally efficient in order to allow sampling posterior probabilities while performing Bayesian estimation of the cosmological parameters. In order to satisfy these requirements, we built a deep neural network model that emulates the convolution with the window function, and we show that it provides fast and accurate predictions. We trained (tested) the network using a suite of 2000 (200) cosmological models within the cold dark matter scenario, and demonstrate that its performance is agnostic to the precise values of the cosmological parameters. In all cases, the deep neural network provides models for the power spectra and the bispectrum that are accurate to better than 0.1 per cent on a timescale of 10 $μ$s.

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arXiv2022

Fitting covariance matrix models to simulations

Data analysis in cosmology requires reliable covariance matrices. Covariance matrices derived from numerical simulations often require a very large number of realizations to be accurate. When a theoretical model for the covariance matrix exists, the parameters of the model can often be fit with many fewer simulations. We write a likelihood-based method for performing such a fit. We demonstrate how a model covariance matrix can be tested by examining the appropriate $χ^2$ distributions from simulations. We show that if model covariance has amplitude freedom, the expectation value of second moment of $χ^2$ distribution with a wrong covariance matrix will always be larger than one using the true covariance matrix. By combining these steps together, we provide a way of producing reliable covariances without ever requiring running a large number of simulations. We demonstrate our method on two examples. First, we measure the two-point correlation function of halos from a large set of $10000$ mock halo catalogs. We build a model covariance with $2$ free parameters, which we fit using our procedure. The resulting best-fit model covariance obtained from just $100$ simulation realizations proves to be as reliable as the numerical covariance matrix built from the full $10000$ set. We also test our method on a setup where the covariance matrix is large by measuring the halo bispectrum for thousands of triangles for the same set of mocks. We build a block diagonal model covariance with $2$ free parameters as an improvement over the diagonal Gaussian covariance. Our model covariance passes the $χ^2$ test only partially in this case, signaling that the model is insufficient even using free parameters, but significantly improves over the Gaussian one.

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arXiv2022

The Covariance of Squeezed Bispectrum Configurations

We measure the halo bispectrum covariance in a large set of N-body simulations and compare it with theoretical expectations. We find a large correlation among (even mildly) squeezed halo bispectrum configurations. A similarly large correlation can be found between squeezed triangles and the long-wavelength halo power spectrum. This shows that the diagonal Gaussian contribution fails to describe, even approximately, the full covariance in these cases. We compare our numerical estimate with a model that includes, in addition to the Gaussian one, only the non-Gaussian terms that are large for squeezed configurations. We find that accounting for these large terms in the modeling greatly improves the agreement of the full covariance with simulations. We apply these results to a simple Fisher matrix forecast, and find that constraints on primordial non-Gaussianity are degraded by a factor of $\sim 2$ when a non-Gaussian covariance is assumed instead of the diagonal, Gaussian approximation.

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arXiv2022

The Halo Bispectrum Multipoles in Redshift Space

We present the analysis of the halo bispectrum in redshift-space in terms of its multipoles, monopole, quadrupole and hexadecapole, measured from a large set of simulations. We fit such measurements with a tree-level model in perturbation theory that depends on linear and nonlinear bias parameters as well as on the growth rate $f$ of density fluctuations. The likelihood analysis takes advantage of a very large set of mock catalogs, enabling a robust estimation of the covariance properties for all multipoles. We compare the numerical estimate of the covariance matrix to its Gaussian prediction finding discrepancies of 10% or less for all configurations with the sole exception of the squeezed triangles in the monopole case. We find the range of validity of the tree-level model, for the total simulation volume of about 1000 $h^{-3}\, {\rm Gpc}^3$, reaches a maximum wavenumber of $0.08 \, h \, {\rm Mpc}^{-1}$ for the monopole, while it is limited to $0.06$ and $0.045\, h \, \rm{Mpc}^{-1}$ respectively for quadrupole and hexadecapole. Despite this, the addition of the quadrupole to the analysis allows for significant improvements on the determination of the model parameters and specifically on $f$, similarly to the power spectrum case. Finally, we compare our numerical estimate for the full covariance with its theoretical prediction in the Gaussian approximation and find the latter to work remarkably well in the context of simulation boxes with periodic boundary condition.

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arXiv2021

Cosmological parameters from the likelihood analysis of the galaxy power spectrum and bispectrum in real space

We present a joint likelihood analysis of the halo power spectrum and bispectrum in real space. We take advantage of a large set of numerical simulations and of an even larger set of halo mock catalogs to provide a robust estimate of the covariance properties. We derive constraints on bias and cosmological parameters assuming a theoretical model from perturbation theory at one-loop for the power spectrum and tree-level for the bispectrum. By means of the Deviance Information Criterion, we select a reference bias model dependent on seven parameters that can describe the data up to $k_{\rm max,P}=0.3\, h \, {\rm Mpc}^{-1}$ for the power spectrum and $k_{\rm max,B}=0.09\, h \, {\rm Mpc}^{-1}$ for the bispectrum at redshift $z=1$. This model is able to accurately recover three selected cosmological parameters even for the rather extreme total simulation volume of $1000\, h^{-3} \, {\rm Gpc}^3$. With the same tools, we study how relations among bias parameters can improve the fit while reducing the parameter space. In addition, we compare common approximations to the covariance matrix against the full covariance estimated from the mocks, and quantify the (non-negligible) effect of ignoring the cross-covariance between the two statistics. Finally, we explore different selection criteria for the triangular configurations to include in the analysis, showing that excluding nearly equilateral triangles rather than simply imposing a fixed maximum $k_{\rm max,B}$ on all triangle sides can lead to a better exploitation of the information contained in the bispectrum.

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arXiv2021

The reach of next-to-leading-order perturbation theory for the matter bispectrum

We provide a comparison between the matter bispectrum derived with different flavours of perturbation theory at next-to-leading order and measurements from an unprecedentedly large suite of $N$-body simulations. We use the $χ^2$ goodness-of-fit test to determine the range of accuracy of the models as a function of the volume covered by subsets of the simulations. We find that models based on the effective-field-theory (EFT) approach have the largest reach, standard perturbation theory has the shortest, and `classical' resummed schemes lie in between. The gain from EFT, however, is less than in previous studies. We show that the estimated range of accuracy of the EFT predictions is heavily influenced by the procedure adopted to fit the amplitude of the counterterms. For the volumes probed by galaxy redshift surveys, our results indicate that it is advantageous to set three counterterms of the EFT bispectrum to zero and measure the fourth from the power spectrum. We also find that large fluctuations in the estimated reach occur between different realisations. We conclude that it is difficult to unequivocally define a range of accuracy for the models containing free parameters. Finally, we approximately account for systematic effects introduced by the $N$-body technique either in terms of a scale- and shape-dependent bias or by boosting the statistical error bars of the measurements (as routinely done in the literature). We find that the latter approach artificially inflates the reach of EFT models due to the presence of tunable parameters.

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arXiv2020

Primordial Non-Gaussianity from Biased Tracers: Likelihood Analysis of Real-Space Power Spectrum and Bispectrum

Upcoming galaxy redshift surveys promise to significantly improve current limits on primordial non-Gaussianity (PNG) through measurements of 2- and 3-point correlation functions in Fourier space. However, realizing the full potential of this dataset is contingent upon having both accurate theoretical models and optimized analysis methods. Focusing on the local model of PNG, parameterized by $f_{\rm NL}$, we perform a Monte-Carlo Markov Chain analysis to confront perturbation theory predictions of the halo power spectrum and bispectrum in real space against a suite of N-body simulations. We model the halo bispectrum at tree-level, including all contributions linear and quadratic in $f_{\rm NL}$, and the halo power spectrum at 1-loop, including tree-level terms up to quadratic order in $f_{\rm NL}$ and all loops induced by local PNG linear in $f_{\rm NL}$. Keeping the cosmological parameters fixed, we examine the effect of informative priors on the linear non-Gaussian bias parameter on the statistical inference of $f_{\rm NL}$. A conservative analysisof the combined power spectrum and bispectrum, in which only loose priors are imposed and all parameters are marginalized over, can improve the constraint on $f_{\rm NL}$ by more than a factor of 5 relative to the power spectrum-only measurement. Imposing a strong prior on $b_ϕ$, or assuming bias relations for both $b_ϕ$ and $b_{ϕδ}$ (motivated by a universal mass function assumption), improves the constraints further by a factor of few. In this case, however, we find a significant systematic shift in the inferred value of $f_{\rm NL}$ if the same range of wavenumber is used. Likewise, a Poisson noise assumption can lead to significant systematics, and it is thus essential to leave all the stochastic amplitudes free.

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arXiv2020

Towards cosmological constraints from the compressed modal bispectrum: a robust comparison of real-space bispectrum estimators

Higher-order clustering statistics, like the galaxy bispectrum, can add complementary cosmological information to what is accessible with two-point statistics, like the power spectrum. While the standard way of measuring the bispectrum involves estimating a bispectrum value in a large number of Fourier triangle bins, the compressed modal bispectrum approximates the bispectrum as a linear combination of basis functions and estimates the expansion coefficients on the chosen basis. In this work, we compare the two estimators by using parallel pipelines to analyze the real-space halo bispectrum measured in a suite of $N$-body simulations corresponding to a total volume of $\sim 1{,}000 \,h^{-3}\,{\rm Gpc}^3$, with covariance matrices estimated from 10,000 mock halo catalogs. We find that the modal bispectrum yields constraints that are consistent and competitive with the standard bispectrum analysis: for the halo bias and shot noise parameters within the tree-level halo bispectrum model up to $k_{\rm max} \approx 0.06 \, (0.10) \,h\,{\rm Mpc}^{-1}$, only 6 (10) modal expansion coefficients are necessary to obtain constraints equivalent to the standard bispectrum estimator using $\sim$ 20 to 1,600 triangle bins, depending on the bin width. For this work, we have implemented a modal estimator pipeline using Markov Chain Monte Carlo simulations for the first time, and we discuss in detail how the parameter posteriors and modal expansion are robust to, or sensitive to, several user settings within the modal bispectrum pipeline. The combination of the highly efficient compression that is achieved and the large number of mock catalogs available allows us to quantify how our modal bispectrum constraints depend on the number of mocks that are used to estimate covariance matrices and the functional form of the likelihood.

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arXiv2020

Toward a robust inference method for the galaxy bispectrum: likelihood function and model selection

The forthcoming generation of galaxy redshift surveys will sample the large-scale structure of the Universe over unprecedented volumes with high-density tracers. This advancement will make robust measurements of three-point clustering statistics possible. In preparation for this improvement, we investigate how several methodological choices can influence inferences based on the bispectrum about galaxy bias and shot noise. We first measure the real-space bispectrum of dark-matter haloes extracted from 298 N-body simulations covering a volume of approximately $1000 h^{-3} \mathrm{Gpc}^3$. We then fit a series of theoretical models based on tree-level perturbation theory to the numerical data. To achieve this, we estimate the covariance matrix of the measurement errors by using 10,000 mock catalogues generated with the Pinocchio code. We study how the model constraints are influenced by the binning strategy for the bispectrum configurations and by the form of the likelihood function. We also use Bayesian model-selection techniques to single out the optimal theoretical description of our data. We find that a three-parameter bias model combined with Poissonian shot noise is necessary to model the halo bispectrum up to scales of $k_\mathrm{max}\lesssim 0.08 h \mathrm{Mpc}^{-1}$, although fitting formulae that relate the bias parameters can be helpful to reduce the freedom of the model without compromising accuracy. Our data clearly disfavour local Eulerian and local Lagrangian bias models and do not require corrections to Poissonian shot noise. We anticipate that model-selection diagnostics will be particularly useful to extend the analysis to smaller scales as, in this case, the number of model parameters will grow significantly large.

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