arXiv subjects

Rossana Ruggeri

Publications and source records attributed to Rossana Ruggeri.

At least 19 recordsLinked to original sources

arXiv2026

Cosmic Pairs: A DESI Census of Dual and Offset AGN as Precursors to Massive Black Hole Binaries

We present a systematic census of dual and offset active galactic nuclei (AGN) using spectroscopic data from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). After correcting for observational systematics, our final sample contains $>7,000$ dual AGN and 27,000 galaxy pairs containing one AGN over the redshift range $0 \lesssim z \lesssim 3.6$. This sample expands the known dual AGN sample by $\sim 1-2$ orders of magnitude at $0.2 \lesssim z \lesssim 0.4$, includes $\sim 50$ dwarf dual AGN candidates in a regime where only a handful were previously known, and triples the census at $z>2$. Dual AGN are preferentially found at small separations, consistent with merger-driven triggering of AGN activity. The two members of a pair differ in their star formation response: the more massive (primary) host changes little with separation, while the less massive (secondary) lies $\sim 0.3$ dex above matched inactive and one-AGN companions at the same projected separation in main-sequence offset. Using ASTRID simulations, we predict that the fraction of DESI dual AGN whose central black holes will merge by $z \sim 0$ increases with redshift, reaching $\sim 76\%$ by $z \sim 2$, while the fraction producing LISA-detectable mergers peaks at $\sim 37\%$ near $z \sim 0.9$. These results provide the largest uniformly selected spectroscopic sample of kpc-scale dual and offset AGN candidates from a single survey, connecting their host-galaxy and AGN demographics to the progenitor population of massive black hole mergers detectable by LISA.

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arXiv2025

A Unified Photometric Redshift Calibration for Weak Lensing Surveys using the Dark Energy Spectroscopic Instrument

The effective redshift distribution $n(z)$ of galaxies is a critical component in the study of weak gravitational lensing. Here, we introduce a new method for determining $n(z)$ for weak lensing surveys based on high-quality redshifts and neural network-based importance weights. Additionally, we present the first unified photometric redshift calibration of the three leading stage-III weak lensing surveys, the Dark Energy Survey (DES), the Hyper Suprime-Cam (HSC) survey and the Kilo-Degree Survey (KiDS), with state-of-the-art spectroscopic data from the Dark Energy Spectroscopic Instrument (DESI). We verify our method using a new, data-driven approach and obtain $n(z)$ constraints with statistical uncertainties of order $σ_{\bar z} \sim 0.01$ and smaller. Our analysis is largely independent of previous photometric redshift calibrations and, thus, provides an important cross-check in light of recent cosmological tensions. Overall, we find excellent agreement with previously published results on the DES Y3 and HSC Y1 data sets while there are some differences on the mean redshift with respect to the previously published KiDS-1000 results. We attribute the latter to mismatches in photometric noise properties in the COSMOS field compared to the wider KiDS SOM-gold catalog. At the same time, the new $n(z)$ estimates for KiDS do not significantly change estimates of cosmic structure growth from cosmic shear. Finally, we discuss how our method can be applied to future weak lensing calibrations with DESI data.

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arXiv2025

Cosmological Constraints from Full-Scale Clustering and Galaxy-Galaxy Lensing with DESI DR1

We present constraints on cosmic structure growth from the analysis of galaxy clustering and galaxy-galaxy lensing with galaxies from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1. We analyze four samples drawn from the Bright Galaxy Survey (BGS) and the Luminous Red Galaxy (LRG) target classes. Projected galaxy clustering measurements from DESI are supplemented with lensing measurements from the Dark Energy Survey (DES), the Kilo-Degree Survey (KiDS), and the Hyper Suprime-Cam (HSC) survey around the same targets. Our method relies on a simulation-based modeling framework using the AbacusSummit simulations and a complex halo occupation distribution model that incorporates assembly bias. We analyze scales down to $0.4 \, h^{-1} \, \mathrm{Mpc}$ for clustering and $2.5 \, h^{-1} \, \mathrm{Mpc}$ for lensing, leading to stringent constraints on $S_8 = σ_8 \sqrt{Ω_\mathrm{m} / 0.3}$ and $Ω_\mathrm{m}$ when fixing other cosmological parameters to those preferred by the CMB. We find $S_8 = 0.794 \pm 0.023$ and $Ω_\mathrm{m} = 0.295 \pm 0.012$ when using lensing measurements from DES and KiDS. Similarly, for HSC, we find $S_8 = 0.793 \pm 0.017$ and $Ω_\mathrm{m} = 0.303 \pm 0.010$ when assuming the best-fit photometric redshift offset suggested by the HSC collaboration. Overall, our results are in good agreement with other results in the literature while continuing to highlight the constraining power of non-linear scales.

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arXiv2025

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform

We present a cosmology analysis of simulated weak lensing convergence maps using the Neural Field Scattering Transform (NFST) to constrain cosmological parameters. The NFST extends the Wavelet Scattering Transform (WST) by incorporating trainable neural field filters while preserving rotational and translational symmetries. This setup balances flexibility with robustness, ideal for learning in limited training data regimes. We apply the NFST to 500 simulations from the CosmoGrid suite, each providing a total of 1000 square degrees of noiseless weak lensing convergence maps. We use the resulting learned field compression to model the posterior over $Ω_m$, $σ_8$, and $w$ in a $w$CDM cosmology. The NFST consistently outperforms the WST benchmark, achieving a 16% increase in the average posterior probability density assigned to test data. Further, the NFST improves direct parameter prediction precision on $σ_8$ by 6% and $w$ by 11%. We also introduce a new visualization technique to interpret the learned filters in physical space and show that the NFST adapts its feature extraction to capture task-specific information. These results establish the NFST as a promising tool for extracting maximal cosmological information from the non-Gaussian information in upcoming large-scale structure surveys, without requiring large simulated training datasets.

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arXiv2025

Learning Intrinsic Alignments from Local Galaxy Environments

We present DELTA (Data-Empiric Learned Tidal Alignments), a deep learning model that isolates galaxy intrinsic alignments (IAs) from weak lensing distortions using only observational data. The model uses an Equivariant Graph Neural Network backbone suitable for capturing information from the local galaxy environment, in conjunction with a probabilistic orientation output. Unlike parametric models, DELTA flexibly learns the relationship between galaxy shapes and their local environments, without assuming an explicit IA form or relying on simulations. When applied to mock catalogs with realistic noisy IAs injected, it accurately reconstructs the noise-free, pure IA signal. Mapping these alignments provides a direct visualization of IA patterns in the mock catalogs. Combining DELTA with deep learning interpretation techniques provides further insights into the physics driving tidal relationships between galaxies. This new approach to understanding and controlling IAs is suitable for application to joint photometric and spectroscopic surveys such as the combination of upcoming Euclid, Rubin, and DESI datasets.

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arXiv2024

Cosmology with shear ratios: a joint study of weak lensing and spectroscopic redshift datasets

The ratio of the average tangential shear signal of different weak lensing source populations around the same lens galaxies, also known as a shear ratio, provides an important test of lensing systematics and a potential source of cosmological information. In this paper we measure shear ratios in three current weak lensing surveys -- KiDS, DES, and HSC -- using overlapping data from the Baryon Oscillation Spectroscopic Survey. We apply a Bayesian method to reduce bias in shear ratio measurement, and assess the degree to which shear ratio information improves the determination of important astrophysical parameters describing the source redshift distributions and intrinsic galaxy alignments, as well as cosmological parameters, in comparison with cosmic shear and full 3x2-pt correlations (cosmic shear, galaxy-galaxy lensing, and galaxy clustering). We consider both Fisher matrix forecasts, as well as full likelihood analyses of the data. We find that the addition of shear ratio information to cosmic shear allows the mean redshifts of the source samples and intrinsic alignment parameters to be determined significantly more accurately. Although the additional constraining power enabled by the shear ratio is less than that obtained by introducing an accurate prior in the mean source redshift using photometric redshift calibration, the shear ratio allows for a useful cross-check. The inclusion of shear ratio data consistently benefits the determination of cosmological parameters such as S_8, for which we obtain improvements up to 34%. However these improvements are less significant when shear ratio is combined with the full 3x2-pt correlations. We conclude that shear ratio tests will remain a useful source of cosmological information and cross-checks for lensing systematics, whose application will be further enhanced by upcoming datasets such as the Dark Energy Spectroscopic Instrument.

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arXiv2024

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological Implications from two Decades of Spectroscopic Surveys at the Apache Point observatory

We present the cosmological implications from final measurements of clustering using galaxies, quasars, and Ly$α$ forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, $r_d$, from eight different samples and six measurements of the growth rate parameter, $fσ_8$, from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, $Λ$CDM model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization the BAO data provide nearly an order of magnitude improvement on curvature constraints. The RSD measurements indicate a growth rate that is consistent with predictions from Planck primary data and with General Relativity. When combining the results of SDSS BAO and RSD with external data, all multiple-parameter extensions remain consistent with a $Λ$CDM model. Regardless of cosmological model, the precision on $Ω_Λ$, $H_0$, and $σ_8$, remains at roughly 1\%, showing changes of less than 0.6\% in the central values between models. The inverse distance ladder measurement under a o$w_0w_a$CDM yields $H_0= 68.20 \pm 0.81 \, \rm km\, s^{-1} Mpc^{-1}$, remaining in tension with several direct determination methods. (abridged)

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arXiv2024

Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform

Recent studies using four-point correlations suggest a parity violation in the galaxy distribution, though the significance of these detections is sensitive to the choice of simulation used to model the noise properties of the galaxy distribution. In a recent paper, we introduce an unsupervised learning approach which offers an alternative method that avoids the dependence on mock catalogs, by learning parity violation directly from observational data. However, the Convolutional Neural Network (CNN) model utilized by our previous unsupervised approach struggles to extend to more realistic scenarios where data is limited. We propose a novel method, the Neural Field Scattering Transform (NFST), which enhances the Wavelet Scattering Transform (WST) technique by adding trainable filters, parameterized as a neural field. We first tune the NFST model to detect parity violation in a simplified dataset, then compare its performance against WST and CNN benchmarks across varied training set sizes. We find the NFST can detect parity violation with $4\times$ less data than the CNN and $32\times$ less than the WST. Furthermore, in cases with limited data the NFST can detect parity violation with up to $6σ$ confidence, where the WST and CNN fail to make any detection. We identify that the added flexibility of the NFST, and particularly the ability to learn asymmetric filters, as well as the specific symmetries built into the NFST architecture, contribute to its improved performance over the benchmark models. We further demonstrate that the NFST is readily interpretable, which is valuable for physical applications such as the detection of parity violation.

astro-ph.IM↗

arXiv2024

Redshift evolution and covariances for joint lensing and clustering studies with DESI Y1

Galaxy-galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) dataset promise to yield unprecedented combined-probe tests of cosmology and the galaxy-halo connection. In such analyses, it is essential to identify and characterise all relevant statistical and systematic errors. In this paper, we forecast the covariances of DESI Y1 GGL+clustering measurements and characterise the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and galaxy-galaxy lensing correlations, we compute a Gaussian analytical covariance, using a suite of N-body and log-normal simulations to characterise the effect of the survey footprint. Using the DESI One Percent Survey data, we measure the evolution of galaxy bias parameters for the DESI Luminous Red Galaxy (LRG) and Bright Galaxy Survey (BGS) samples. We find mild evolution in the LRGs in 0.4 < z < 0.8, subdominant compared to the expected statistical errors. For BGS, we find less evolution effects for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that with a fiducial redshift bin width delta z = 0.1, evolution effects on GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins, given DESI Y1 sample size. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of 0.1Mpc/h. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample

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arXiv2023

Cross-correlating radial peculiar velocities and CMB lensing convergence

We study, for the first time, the cross correlation between the angular distribution of radial peculiar velocities (PV) and the lensing convergence of cosmic microwave background (CMB) photons. We derive theoretical expectations for the signal and its covariance and assess its detectability with existing and forthcoming surveys. We find that such cross-correlations are expected to improve constraints on different gravitational models by partially breaking degeneracies with the matter density. We identify in the distance-scaling dispersion of the peculiar velocities the most relevant source of noise in the cross correlation. For this reason, we also study how the above picture changes assuming a redshift-independent scatter for the PV, obtained for example using a reconstruction technique. Our results show that the cross correlation might be detected in the near future combining PV measurements from DESI and the convergence map from CMB-S4. Using realistic direct PV measurements we predict a cumulative signal-to-noise ratio of approximately $3.8 σ$ using data on angular scales $3 \leq \ell \leq 200$. For an idealized reconstructed peculiar velocity map extending up to redshift $z=0.15$ and a smoothing scale of $4$ Mpc $h^{-1}$ we predict a cumulative signal-to-noise ratio of approximately $ 27 σ$ from angular scales $3 \leq \ell \leq200 $. We conclude that currently reconstructed peculiar velocities have more constraining power than directly observed ones, even though they are more cosmological-model dependent.

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arXiv2023

Synthetic light cone catalogues of modern redshift and weak lensing surveys with AbacusSummit

The joint analysis of different cosmological probes, such as galaxy clustering and weak lensing, can potentially yield invaluable insights into the nature of the primordial Universe, dark energy and dark matter. However, the development of high-fidelity theoretical models that cover a wide range of scales and redshifts is a necessary stepping-stone. Here, we present public high-resolution weak lensing maps on the light cone, generated using the $N$-body simulation suite AbacusSummit in the Born approximation, and accompanying weak lensing mock catalogues, tuned via fits to the Early Data Release small-scale clustering measurements of the Dark Energy Spectroscopic Instrument (DESI). Available in this release are maps of the cosmic shear, deflection angle and convergence fields at source redshifts ranging from $z = 0.15$ to 2.45 with $Δz = 0.05$ as well as CMB convergence maps ($z \approx 1090$) for each of the 25 ${\tt base}$-resolution simulations ($L_{\rm box} = 2000\,h^{-1}{\rm Mpc}$, $N_{\rm part} = 6912^3$) as well as for the two ${\tt huge}$ simulations ($L_{\rm box} = 7500\,h^{-1}{\rm Mpc}$, $N_{\rm part} = 8640^3$) at the fiducial AbacusSummit cosmology ($Planck$ 2018). The pixel resolution of each map is 0.21 arcmin, corresponding to a HEALPiX $N_{\rm side}$ of 16384. The sky coverage of the ${\tt base}$ simulations is an octant until $z \approx 0.8$ (decreasing to about 1800 deg$^2$ at $z \approx 2.4$), whereas the ${\tt huge}$ simulations offer full-sky coverage until $z \approx 2.2$. Mock lensing source catalogues are sampled matching the ensemble properties of the Kilo-Degree Survey, Dark Energy Survey, and Hyper-Suprime Cam weak lensing datasets. The produced mock catalogues are validated against theoretical predictions for various clustering and lensing statistics such as galaxy clustering multipoles, galaxy-shear and shear-shear, showing excellent agreement.

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arXiv2023

Testing modified gravity scenarios with direct peculiar velocities

The theoretical basis of dark energy remains unknown and could signify a need to modify the laws of gravity on cosmological scales. In this study we investigate how the clustering and motions of galaxies can be used as probes of modified gravity theories, using galaxy and direct peculiar velocity auto- and cross-correlation functions. We measure and fit these correlation functions in simulations of $Λ$CDM, DGP, and $f(R)$ cosmologies and, by extracting the characteristic parameters of each model, we show that these theories can be distinguished from General Relativity using these measurements. We present forecasts showing that with sufficiently large data samples, this analysis technique is a competitive probe that can help place limits on allowed deviations from GR. For example, a peculiar velocity survey reaching to $z=0.5$ with $20\%$ distance accuracy would constrain model parameters to 3-$σ$ confidence limits $\log_{10}|f_{R0}| < -6.45$ for $f(R)$ gravity and $r_c > 2.88 \, c/H_0$ for nDGP, assuming a fiducial GR model.

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arXiv2022

A local measurement of the growth rate from peculiar velocities and galaxy clustering correlations in the 6dF Galaxy Survey

Galaxy peculiar velocities provide an integral source of cosmological information that can be harnessed to measure the growth rate of large scale structure and constrain possible extensions to General Relativity. In this work, we present a method for extracting the information contained within galaxy peculiar velocities through an ensemble of direct peculiar velocity and galaxy clustering correlation statistics, including the effects of redshift space distortions, using data from the 6-degree Field Galaxy Survey. Our method compares the auto- and cross-correlation function multipoles of these observables, with respect to the local line of sight, with the predictions of cosmological models. We find that the uncertainty in our measurement is improved when combining these two sources of information in comparison to fitting to either peculiar velocity or clustering information separately. When combining velocity and density statistics in the range $27 < s < 123 \, h^{-1}$ Mpc we obtain a value for the local growth rate of $fσ_8 = 0.358 \pm 0.075$ and for the linear redshift distortion parameter $β= 0.298 \pm 0.065$, recovering both with $20.9$ per cent and $21.8$ per cent accuracy respectively. We conclude this work by comparing our measurement with other recent local measurements of the growth rate, spanning different datasets and methodologies. We find that our results are in broad agreement with those in the literature and are fully consistent with $Λ$CDM cosmology. Our methods can be readily scaled to analyse upcoming large galaxy surveys and achieve accurate tests of the cosmological model.

astro-ph.CO↗

arXiv2022

A data compression and optimal galaxy weights scheme for Dark Energy Spectroscopic Instrument and weak lensing datasets

Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. The galaxy redshift sample from the Dark Energy Spectroscopic Instrument (DESI) will have a significant overlap with major ongoing imaging surveys specifically designed for weak lensing measurements: the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES) and the Hyper Suprime-Cam (HSC) survey. In this work we analyse simulated redshift and lensing catalogues to establish a new strategy for combining high-quality cosmological imaging and spectroscopic data, in view of the first-year data assembly analysis of DESI. In a test case fitting for a reduced parameter set, we employ an optimal data compression scheme able to identify those aspects of the data that are most sensitive to the cosmological information, and amplify them with respect to other aspects of the data. We find this optimal compression approach is able to preserve all the information related to the growth of structure; we also extend this scheme to derive weights to be applied to individual galaxies, and show that these produce near-optimal results.

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arXiv2021

The clustering of galaxies in the completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Primordial non-Gaussianity in Fourier Space

We present measurements of the local primordial non-Gaussianity parameter \fNLloc from the clustering of 343,708 quasars with redshifts 0.8 < z < 2.2 distributed over 4808 square degrees from the final data release (DR16) of the extended Baryon acoustic Oscillation Spectroscopic Survey (eBOSS), the largest volume spectroscopic survey up to date. Our analysis is performed in Fourier space, using the power spectrum monopole at very large scales to constrain the scale dependent halo bias. We carefully assess the impact of systematics on our measurement and test multiple contamination removal methods. We demonstrate the robustness of our analysis pipeline with EZ-mock catalogues that simulate the eBOSS DR16 target selection. We find $f_\mathrm{NL}=-12\pm 21$ (68\% confidence) for the main clustering sample including quasars with redshifts between 0.8 and 2.2, after exploiting a novel neural network scheme for cleaning the DR16 sample and in particular after applying redshift weighting techniques, designed for non-Gaussianity measurement from large scales structure, to optimize our analysis, which improve our results by 37\%.

astro-ph.CO↗

arXiv2021

Improving estimates of the growth rate using galaxy-velocity correlations: a simulation study

We present an improved framework for estimating the growth rate of large-scale structure, using measurements of the galaxy-velocity cross-correlation in configuration space. We consider standard estimators of the velocity auto-correlation function, $ψ_1$ and $ψ_2$, the two-point galaxy correlation function, $ξ_{gg}$, and introduce a new estimator of the galaxy-velocity cross-correlation function, $ψ_3$. By including pair counts measured from random catalogues of velocities and positions sampled from distributions characteristic of the true data, we find that the variance in the galaxy-velocity cross-correlation function is significantly reduced. Applying a covariance analysis and $χ^2$ minimisation procedure to these statistics, we determine estimates and errors for the normalised growth rate $fσ_8$ and the parameter $β= f/b$, where $b$ is the galaxy bias factor. We test this framework on mock hemisphere datasets for redshift $z < 0.1$ with realistic velocity noise constructed from the L-PICOLA simulation code, and find that we are able to recover the fiducial value of $fσ_8$ from the joint combination of $ψ_1$ + $ψ_2$ + $ψ_3$ + $ξ_{gg}$, with 15\% accuracy from individual mocks. We also recover the fiducial $fσ_8$ to within 1$σ$ regardless of the combination of correlation statistics used. When we consider all four statistics together we find that the statistical uncertainty in our measurement of the growth rate is reduced by $59\%$ compared to the same analysis only considering $ψ_2$, by $53\%$ compared to the same analysis only considering $ψ_1$, and by $52\%$ compared to the same analysis jointly considering $ψ_1$ and $ψ_2$.

astro-ph.CO↗

arXiv2020

Compressing combined probes: redshift weights for joint lensing and clustering analyses

Combining different observational probes, such as galaxy clustering and weak lensing, is a promising technique for unveiling the physics of the Universe with upcoming dark energy experiments. Whilst this strategy significantly improves parameter constraints, decreasing the degeneracies of individual analyses and controlling the systematics, processing data from tens of millions of galaxies is not a trivial task. In this work we derive and test a new estimator for joint clustering and lensing data analysis, maximising the scientific return and decreasing the computational cost. Our estimator compresses the data by up-weighting the components most sensitive to the parameters of interest, with no loss of information, taking into account information from the cross-correlation between the two probes. We derive optimal redshift weights which may be applied to individual galaxies when testing a given statistic and cosmological model.

astro-ph.CO↗

arXiv2019

How accurately can we measure the baryon acoustic oscillation feature?

Baryon acoustic oscillations (BAO) represent one of the cleanest probes of dark energy, allowing for tests of the cosmological model through the measurement of distance and expansion rate from a 3D galaxy distribution. The signal appears at large scales in the correlation function where linear theory applies, allowing for the construction of accurate models. However, due to the lower number of modes available at these scales, sample variance has a significant impact on the signal, and may sharpen or widen the underlying peak. Therefore, equivalent mock realizations of a galaxy survey present different errors in the position of the peak when uncertainties are estimated from the posterior probability distribution corresponding to the individual mocks. Hence the posterior width, often quoted as the error in BAO survey measurements, is subject to sample noise. A different definition of the error is provided by the asymptotic variance of the maximum likelihood estimator, which involves the average over multiple realizations, and is not subject to sample noise. In this work we re-analyse the main galaxy survey data available for BAO measurements and quantify the impact of the noise component on the error quoted for BAO measurements. We quantify the difference between three definitions of the error: the confidence region computed from a single posterior, the average of the variances of many realizations, and the Fisher matrix prediction assuming a Gaussian likelihood.

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