arXiv subjects

Alfonso Veropalumbo

Publications and source records attributed to Alfonso Veropalumbo.

At least 19 recordsLinked to original sources

arXiv2026

First full-shape joint analysis of the two- and three-point correlation functions on real data: $\Lambda$CDM cosmological constraints from BOSS DR12

The three-point correlation function (3PCF) encodes cosmological information beyond the two-point correlation function (2PCF), yet a full-shape joint analysis in redshift space using real data has so far been lacking. We present the first full-shape cosmological constraints from a joint analysis of 2PCF and 3PCF in redshift space, using BOSS DR12 data, extending to real data, including Alcock--Paczy\'nski and redsfhit space distortions, the full-shape configuration-space framework validated in real space for the first time by Euclid Collaboration: Guidi et al (2026). We model both statistics adopting the velocity difference generating function (VDG) framework, incorporating non-perturbative Fingers-of-God damping, a complete Eulerian galaxy bias expansion, and infrared resummation. Fast and accurate theoretical predictions are obtained using dedicated emulators, which enable a full-shape likelihood analysis of the 3PCF and its combination with the 2PCF, varying the cosmological parameters $10^9 A_s, \omega_{\rm cdm}$ and $h$, while the baryon fraction density $\omega_{b}$ is fixed to its fiducial value. The covariance matrix is estimated from 2048 MultiDark-Patchy mocks, and an optimal data-vector compression ensures a stable covariance inversion. The perturbative model is validated against goodness-of-fit tests across different scales, and provides a good description of the joint data vector down to $r_{\rm min}^{\rm 3PCF} \sim 60\,h^{-1}{\rm Mpc}$. We find that the joint 2PCF+3PCF analysis yields significant improvements over the 2PCF-only baseline, with gains of approximately 29\%, 10\%, and 24\% on $\sigma(h)$, $\sigma(\omega_{\rm cdm})$, and $\sigma(A_s)$, respectively. The improvements mainly arise from the additional BAO cosmological information encoded in the 3PCF triangle configurations.

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arXiv2026

Denoising clustering covariance matrices with Rotational Invariant Estimators

Cosmological parameter inference from galaxy clustering relies critically on accurate estimates of the covariance and precision matrices. These are often obtained from a limited number of mock catalogs, introducing noise and bias in the precision matrix when the data-vector dimension becomes comparable to the number of available realizations. We present the first application of the Rotational Invariant Estimator (RIE) to the large-scale clustering of galaxies, benchmarking it against the standard sample covariance and the non-linear shrinkage estimator NERCOME for both the two-point correlation function (2PCF) and power spectrum. Using controlled synthetic data sets with analytically known covariance matrices, we estimate the covariance with all three methods across a range of mock-to-dimension ratios $q = N/D$ and data-vector sizes $D$. We then perform Bayesian inference with an EFT-based model and quantify each estimator through the Figure of Bias (FoB) and Figure of Merit (FoM). After correction for finite-$N$ effects, the sample covariance recovers unbiased average uncertainty volumes but suffers from growing best-fit scatter and bias at small $q$ due to the Dodelson--Schneider effect. Both NERCOME and RIE substantially reduce these stochastic shifts; however, the uncertainties they assign are probe-dependent. In configuration space, both estimators can yield overly tight constraints, with a bias that grows with $D$. In Fourier space, RIE delivers markedly improved best-fit stability with only mild FoM bias, whereas NERCOME tends to overestimate the constraining power. Among the estimators tested, RIE emerges as the most effective at stabilizing best-fit recovery, particularly in Fourier space, where it closely reproduces the reference posteriors even when the number of mocks barely exceeds the data-vector dimension.

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arXiv2025

The imprints of massive neutrinos on the three-point correlation function of large-scale structures

Free-streaming of cosmic neutrinos affects the distribution and growth of cosmic structures on small scales. This enables the sum of neutrino masses $M_\nu$ to be constrained from clustering studies. We investigate the possibility of disentangling massive neutrino cosmologies with the three-point correlation function (3PCF) for the first time. We measured the isotropic connected 3PCF $\zeta$ and the reduced 3PCF $Q$ of halo catalogs from the Quijote suite of $N$-body simulations, considering $M_\nu =0.0, 0.1, 0.2,$ and $0.4 \, \mathrm{eV}$ in different redshift bins. We developed a framework to quantify the detectability of massive neutrinos for different triangle configurations and shapes, and applied it to a case compatible with a stage-IV spectroscopic survey. We also compared our results with the analysis of simulations without neutrinos, but with different $\sigma_8$ values, to test whether the 3PCF can break the well-known degeneracy between the two parameters. We found that as a result of free-streaming, the strongest signal is found for quasi-isosceles and squeezed triangles; this signal increases for decreasing redshifts. Among these configurations, elongated triangles, tracing the filamentary structure of the cosmic web, are the most affected by massive neutrinos, with a 3PCF signal increasing with $M_\nu$. A complementary source of signal comes from right-angled triangles in $Q$. Importantly, we found that the signatures of a $\sigma_8$ variation appear to be significantly different on elongated triangles in $\zeta$ and right-angled triangles in $Q$, suggesting that the 3PCF can be used to effectively break the $M_\nu - \sigma_8$ degeneracy. These results open the possibility to use the 3PCF as a powerful complementary tool for constraining neutrino masses in current and future spectroscopic surveys such as DESI, Euclid, 4MOST, and the Nancy Grace Roman Space Telescope.

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arXiv2024

Modeling and measuring the anisotropic halo 3-point correlation function: a coordinated study

Ongoing and future spectroscopic galaxy surveys will cover unprecedented volumes with a number of objects large enough to effectively probe clustering anisotropies through higher-order statistics. In this work, we present a novel and efficient implementation of both a model for the multipole moments of the anisotropic 3-point correlation function (3PCF) and of their estimator. To evaluate the performance of our model, we compared its predictions against direct 3PCF measurements obtained with our estimator from a set of 298 dark matter halo catalogs drawn from the $z=1$ snapshots of $N$-body simulations. For the statistical analysis, we employed a covariance matrix estimated from an independent suite of 3000 mock halo catalogs at the same redshift. We then repeated the analysis by combining the 2-point correlation function (2PCF) to the 3PCF, with and without including its anisotropic part. In the 3PCF-only analysis, the addition of the anisotropic component of the 3PCF effectively breaks the degeneracy between the growth rate $f$ and the linear bias $b_1$, significantly reducing their uncertainties. It also significantly improves the precision of the Alcock-Paczynski parameter $\varepsilon$ but does not reduce the $\sim 1$% offset we find in the estimate of the isotropic dilation parameter $\alpha$. The joint 2PCF+3PCF analysis reduces, though does not fully remove, biases in the AP and isotropic dilation parameters and breaks the $f$-$b_1$-$\sigma_8$ degeneracy, leading to tighter constraints overall. The anisotropic 3PCF adds little to the joint analysis because the tree-level 3PCF model fails to capture the anisotropic information primarily encoded on small scales and in squeezed triangle configurations. A more advanced model will be required to exploit this information fully.

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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 $\chi^2$ distributions from simulations. We show that if model covariance has amplitude freedom, the expectation value of second moment of $\chi^2$ 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 $\chi^2$ 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

Tracing the environmental history of observed galaxies via extended fast action minimization method

We present a novel application of the extended Fast Action Minimization method (eFAM) aimed at assessing the role of the environment in shaping galaxy evolution. We validate our approach by testing eFAM predictions against the Magneticum hydrodynamical simulation. We consider the z~0 snapshot of the simulation as our observed catalogue and use the reconstructed trajectories of galaxies to model the evolution of cosmic structures. At the statistical level, the fraction of volume (VFF) occupied by voids, sheets, filaments, and clusters in the reconstructed catalogues agrees within $1\sigma$ with the VFF estimated from the high-redshift snapshots of the simulation. The local accuracy of eFAM structures is evaluated by computing their purity with respect to the simulated catalogues, P, at the cells of a regular grid. Up to z=1.2, clusters have 0.58<P<0.93, filaments vary in 0.90<P<0.99, sheets show 0.78<P<0.92, and voids are best identified with 0.90<P<0.92. As redshift increases, comparing reconstructed tracers and simulated galaxies becomes more difficult due to their different biases and number densities and the purity decreases to P~0.6. We retrieve the environmental history of individual galaxies by tracing their trajectories through the cosmic web and relate their observed gas fraction, $f_\mathrm{gas}$, with the time spent within different structures. For galaxies in clusters and filaments, eFAM reproduces the variation of $f_\mathrm{gas}$ as a function of the redshift of accretion/infall as traced by the simulations with a 1.5 $\sigma$ statistical agreement (which decreases to 2.5 $\sigma$ statistical agreement for low-mass galaxies in filaments). These results support the application of eFAM to observational data to study the environmental dependence of observed galaxy properties, offering a complementary approach to that based on light-cone observations.

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arXiv2022

New constraints on primordial features from the galaxy two-point correlation function

Features in the primordial power spectrum represent the imprinted signal in the density perturbations of the physics and evolution of the early Universe. A measurement of such signals will represents the need to go beyond the minimal assumption made for the initial conditions of the cosmological perturbations. For the first time, we study different templates with undamped oscillations or a bump from the two-point correlation function measured from BOSS DR12 galaxies constraining the amplitude of the features to be at most a few percent. Constraints are competitive to the ones obtained with {\em Planck} DR3.

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arXiv2021

C$^3$-Cluster Clustering Cosmology I. New constraints on the cosmic growth rate at z~0.3 from redshift-space clustering anisotropies

Redshift-space distortions in the clustering of galaxy clusters provide a novel probe to test the gravity theory on cosmological scales. The aim of this work is to derive new constraints on the linear growth rate of cosmic structures from the redshift-space two-point correlation function of galaxy clusters. We construct a large spectroscopic catalogue of optically-selected clusters from the Sloan Digital Sky Survey. The selected sample consists of $43,743$ clusters in the redshift range $0.1<z<0.42$, with masses estimated from weak-lensing calibrated scaling relations. We measure the transverse and radial wedges of the two-point correlation function of the selected clusters. Modelling the redshift-space clustering anisotropies, we provide the first constraints on the linear growth rate from cluster clustering. The cluster masses are used to set a prior on the linear bias of the sample. This represents the main advantage in using galaxy clusters as cosmic probes, instead of galaxies. Assuming a standard cosmological model consistent with the latest cosmic microwave background constraints, we do not find any evidence of deviations from General Relativity. Specifically, we get the value of the growth rate times the matter power spectrum normalisation parameter $fσ_{8}=0.44\pm0.05$, at an effective redshift $z=0.275$.

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arXiv2021

The XXL survey: XLVI. Forward cosmological analysis of the C1 cluster sample

We present the forward cosmological analysis of an $XMM$ selected sample of galaxy clusters out to a redshift of unity. Following our previous 2018 study based on the dn/dz quantity alone, we perform an upgraded cosmological analysis of the same XXL C1 cluster catalogue (178 objects), with a detailed account of the systematic errors. We follow the ASpiX methodology: the distribution of the observed X-ray properties of the cluster population is analysed in a 3D observable space (count rate, hardness ratio, redshift) and modelled as a function of cosmology. Compared to more traditional methods, ASpiX allows the inclusion of clusters down to a few tens of photons. We obtain an improvement by a factor of 2 compared to the previous analysis by letting the normalisation of the M-T relation and the evolution of the L-T relation free. Adding constraints from the XXL cluster 2-point correlation function and the BAO from various surveys decreases the uncertainties by 23 and 53 % respectively, and 62% when adding both. Switching to the scaling relations from the Subaru analysis, and letting free more parameters, our final constraints are $σ_8$ = $0.99^{+0.14}_{-0.23}$, $Ω_m$ = 0.296 $\pm$ 0.034 ($S_8 = 0.98^{+0.11}_{-0.21}$) for the XXL sample alone. Finally, we combine XXL ASpiX, the XXL cluster 2-point correlation function and the BAO, with 11 free parameters, allowing for the cosmological dependence of the scaling relations in the fit. We find $σ_8$ = $0.793^{+0.063}_{-0.12}$, $Ω_m$ = 0.364 $\pm$ 0.015 ($S_8 = 0.872^{+0.068}_{-0.12}$), but still compatible with Planck CMB at 2.2$σ$. The results obtained by the ASpiX method are promising; further improvement is expected from the final XXL cosmological analysis involving a cluster sample twice as large. Such a study paves the way for the analysis of the eROSITA and future Athena surveys.

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arXiv2021

Artificial Neural Networks for Galaxy Clustering. Learning from the two-point correlation function of BOSS galaxies

The increasingly large amount of cosmological data coming from ground-based and space-borne telescopes requires highly efficient and fast enough data analysis techniques to maximise the scientific exploitation. In this work, we explore the capabilities of supervised machine learning algorithms to learn the properties of the large-scale structure of the Universe, aiming at constraining the matter density parameter, Omega m. We implement a new Artificial Neural Network for a regression data analysis, and train it on a large set of galaxy two-point correlation functions in standard cosmologies with different values of Omega m. The training set is constructed from log-normal mock catalogues which reproduce the clustering of the Baryon Oscillation Spectroscopic Survey (BOSS) galaxies. The presented statistical method requires no specific analytical model to construct the likelihood function, and runs with negligible computational cost, after training. We test this new Artificial Neural Network on real BOSS data, finding Omega m=0.309p/m0.008, which is remarkably consistent with standard analysis results.

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arXiv2021

AMICO galaxy clusters in KiDS-DR3: cosmological constraints from large-scale stacked weak lensing profiles

Context. The large-scale mass distribution around dark matter haloes hosting galaxy clusters provides sensitive cosmological information. Aims. In this work, we make use of a large photometric galaxy cluster sample, constructed from the public Third Data Release of the Kilo-Degree Survey, and the corresponding shear signal, to assess cluster masses and test the concordance $Λ$-cold dark matter ($Λ$CDM) model. In particular, we study the weak gravitational lensing effects on scales beyond the cluster virial radius, where the signal is dominated by correlated and uncorrelated matter density distributions along the line-of-sight. The analysed catalogue consists of 6962 galaxy clusters, in the redshift range $0.1 \leq z \leq 0.6$ and with signal-to-noise ratio larger than 3.5. Methods. We perform a full Bayesian analysis to model the stacked shear profiles of these clusters. The adopted likelihood function considers both the small-scale 1-halo term, used primarily to constrain the cluster structural properties, and the 2-halo term, that can be used to constrain cosmological parameters. Results. We find that the adopted modelling is successful to assess both the cluster masses and the total matter density parameter, $Ω_M$, when fitting shear profiles up to the largest available scales of 35 Mpc/h. Moreover, our results provide a strong observational evidence of the 2-halo signal in the stacked gravitational lensing of galaxy clusters, further demonstrating the reliability of this probe for cosmological studies. The main result of this work is a robust constraint on $Ω_M$, assuming a flat $Λ$CDM cosmology. We get $Ω_M = 0.29 \pm 0.02$, estimated from the full posterior probability distribution, consistent with the estimates from cosmic microwave background experiments.

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arXiv2021

Cosmic voids in modified gravity models with massive neutrinos

Cosmic voids are progressively emerging as a new viable cosmological probe. Their abundance and density profiles are sensitive to modifications of gravity, as well as to dark energy and neutrinos. The main goal of this work is to investigate the possibility of exploiting cosmic void statistics to disentangle the degeneracies resulting from a proper combination of $f(R)$ modified gravity and neutrino mass. We use N-body simulations to analyse the density profiles and size function of voids traced by both dark matter particles and haloes. We find clear evidence of the enhancement of gravity in $f(R)$ cosmologies in the void density profiles at $z=1$. However, these effects can be almost completely overridden by the presence of massive neutrinos because of their thermal free-streaming. Despite the limited volume of the analysed simulations does not allow us to achieve a statistically relevant abundance of voids larger than $40 \ \mathrm{Mpc}/h$, we find that the void size function at high redshifts and for large voids is potentially an effective probe to disentangle these degenerate cosmological models, which is key in the prospective of the upcoming wide field redshift surveys.

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arXiv2020

C$^3$-Cluster Clustering Cosmology II. First detection of the BAO peak in the three-point correlation function of galaxy clusters

Third-order statistics of the cosmic density field provides a powerful cosmological probe containing synergistic information to the more commonly explored second-order statistics. Here, we exploit a spectroscopic catalog of 72,563 clusters of galaxies extracted from the Sloan Digital Sky Survey, providing the first detection of the baryon acoustic oscillations (BAO) peak in the three-point correlation function (3PCF) of galaxy clusters. We measure and analyze both the connected and the reduced 3PCF of SDSS clusters from intermediate ($r\sim10$ Mpc/h) up to large ($r\sim140$ Mpc/h) scales, exploring a variety of different configurations. From the analysis of reduced 3PCF at intermediate scales, in combination with the analysis of the two-point correlation function, we constrain both the cluster linear and non-linear bias parameters, $b_1=2.75\pm0.03$ and $b_2=1.2\pm0.5$. We analyze the measurements of the 3PCF at larger scales, comparing them with theoretical models. The data show clear evidence of the BAO peak in different configurations, which appears more visible in the reduced 3PCF rather than in the connected one. From the comparison between theoretical models considering or not the BAO peak, we obtain a quantitative estimate of this evidence, with a $Δχ^2$ between 2 and 75, depending on the considered configuration. Finally, we set up a generic framework to estimate the expected signal-to-noise ratio of the BAO peak in the 3PCF exploring different possible definitions, that can be used to forecast the most favorable configurations to be explored also in different future surveys, and applied it to the case of the Euclid mission.

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arXiv2020

Validating the methodology for constraining the linear growth rate from clustering anisotropies

Redshift-space clustering distortions provide one of the most powerful probes to test the gravity theory on the largest cosmological scales. We perform a systematic validation study of the state-of-the-art statistical methods currently used to constrain the linear growth rate from redshift-space distortions in the galaxy two-point correlation function. The numerical pipelines are tested on mock halo catalogues extracted from large N-body simulations of the standard cosmological framework. We consider both the monopole and quadrupole multipole moments of the redshift-space two-point correlation function, as well as the radial and transverse clustering wedges, in the comoving scale range $10 30$ \Mpch, the discrepancy is reduced below $5\%$. At higher redshifts, we find instead an overall good agreement between measurements and model predictions. Though this accuracy is good enough for clustering analyses in current redshift surveys, the models have to be further improved not to introduce significant systematics in RSD constraints from next generation galaxy surveys. The effect of redshift errors is degenerate with the one of small-scale random motions, and can be marginalised over in the statistical analysis, not introducing any statistically significant bias in the linear growth constraints, especially at $z\geq1$.

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arXiv2019

Cosmological exploitation of the size function of cosmic voids identified in the distribution of biased tracers

Cosmic voids are large underdense regions that, together with galaxy clusters, filaments and walls, build up the large-scale structure of the Universe. The void size function provides a powerful probe to test the cosmological framework. However, to fully exploit this statistics, the void sample has to be properly cleaned from spurious objects. Furthermore, the bias of the mass tracers used to detect these regions has to be taken into account in the size function model. In our work we test a cleaning algorithm and a new void size function model on a set of simulated dark matter halo catalogues, with different mass and redshift selections, to investigate the statistics of voids identified in a biased mass density field. We then investigate how the density field tracers' bias affects the detected size of voids. The main result of this analysis is a new model of the size function, parameterised in terms of the linear effective bias of the tracers used, which is straightforwardly inferred from the large-scale two-point correlation function. This represents a crucial step to exploit the method on real data catalogues. The proposed size function model has been accurately calibrated on mock catalogues, and used to validate the possibility to provide forecasts on the cosmological constraints, namely on the matter density contrast, $Ω_{\rm M}$, and on the normalisation of the linear matter power spectrum, $σ_8$, at different redshifts.

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arXiv2019

Clustering and redshift-space distortions in modified gravity models with massive neutrinos

Modified gravity and massive neutrino cosmologies are two of the most interesting scenarios that have been recently explored to account for possible observational deviations from the concordance $Λ$-cold dark matter ($Λ$CDM) model. In this context, we investigated the large-scale structure of the Universe by exploiting the \dustp simulations that implement, simultaneously, the effects of $f(R)$ gravity and massive neutrinos. To study the possibility of breaking the degeneracy between these two effects, we analysed the redshift-space distortions in the clustering of dark matter haloes at different redshifts. Specifically, we focused on the monopole and quadrupole of the two-point correlation function, both in real and redshift space. The deviations with respect to $Λ$CDM model have been quantified in terms of the linear growth rate parameter. We found that redshift-space distortions provide a powerful probe to discriminate between $Λ$CDM and modified gravity models, especially at high redshifts ($z \gtrsim 1$), even in the presence of massive neutrinos.

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arXiv2018

Gravitational lensing detection of an extremely dense environment around a galaxy cluster

Galaxy clusters form at the highest density nodes of the cosmic web. The clustering of massive halos is enhanced relative to the general mass distribution and matter beyond the virial region is strongly correlated to the halo mass (halo bias). Clustering can be further enhanced depending on halo properties other than mass (secondary bias). The questions of how much and why the regions surrounding rich clusters are over-dense are still unanswered. Here, we report the analysis of the environment bias in a sample of very massive clusters, selected through the Sunyaev-Zel'dovich effect by the Planck mission. We present the first detection of the correlated dark matter associated to a single cluster, PSZ2 G099.86+58.45. The system is extremely rare in the current paradigm of structure formation. The gravitational lensing signal was traced up to 30 megaparsecs with high signal-to-noise ratio ~3.4. The measured shear is very large and points at environment matter density in notable excess of the cosmological mean. The boosting of the correlated dark matter density around high mass halos can be very effective. Together with ensemble studies of the large scale structure, lensing surveys can picture the surroundings of single haloes.

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arXiv2017

Redshift-space distortions of galaxies, clusters and AGN: testing how the accuracy of growth rate measurements depends on scales and sample selections

Redshift-space clustering anisotropies caused by cosmic peculiar velocities provide a powerful probe to test the gravity theory on large scales. However, to extract unbiased physical constraints, the clustering pattern has to be modelled accurately, taking into account the effects of non-linear dynamics at small scales, and properly describing the link between the selected cosmic tracers and the underlying dark matter field. We use a large hydrodynamic simulation to investigate how the systematic error on the linear growth rate, $f$, caused by model uncertainties, depends on sample selections and comoving scales. Specifically, we measure the redshift-space two-point correlation function of mock samples of galaxies, galaxy clusters and Active Galactic Nuclei, extracted from the Magneticum simulation, in the redshift range 0.2 < z < 2, and adopting different sample selections. We estimate $fσ_8$ by modelling both the monopole and the full two-dimensional anisotropic clustering, using the dispersion model. We find that the systematic error on $fσ_8$ depends significantly on the range of scales considered for the fit. If the latter is kept fixed, the error depends on both redshift and sample selection, due to the scale-dependent impact of non-linearities, if not properly modelled. On the other hand, we show that it is possible to get unbiased constraints on $fσ_8$ provided that the analysis is restricted to a proper range of scales, that depends non trivially on the properties of the sample. This can have a strong impact on multiple tracers analyses, and when combining catalogues selected at different redshifts.

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