arXiv subjects

Marina S. Cagliari

Publications and source records attributed to Marina S. Cagliari.

6 recordsLinked to original sources

arXiv2026

Testing parity with composite-field spectra of BOSS and DESI luminous red galaxies

Detection of parity violation on cosmological scales would have profound implications for fundamental physics. Motivated in part by recent measurements of parity-odd four-point correlation functions in BOSS and DESI luminous red galaxy samples, which probe parity violation in the scalar sector, we present the first measurement of parity-odd kurto spectra in spectroscopic galaxy survey data. We analyse two composite-field spectra, $\mathcal{P}_{2\times2}$ (vector--pseudo-vector) and $\mathcal{P}_{3\times1}$ (scalar--pseudo-scalar). Compared with parity-odd four-point correlation function analyses, the kurto-spectrum formalism performs physically motivated compression on the trispectrum into a substantially lower-dimensional data vector, allowing direct estimation of covariance matrices from mock catalogues and reducing sensitivity to covariance-modelling systematics. Using null-hypothesis $χ^2$ tests and cross-patch consistency checks, we find no evidence for a cosmological parity-violating signal in either survey. We examine the impact of the adopted mock catalogues and find that the high-fidelity mocks provide a better match to the data of both surveys than the approximate mocks. The DESI DR1 measurements exhibit a scatter smaller than that of BOSS DR12 by about a factor of four, consistent with the improved statistical precision expected from the higher tracer number density. Future DESI data releases, with larger volume and number density, together with larger suites of high-fidelity mocks, can enable significantly sharper tests of parity violation using kurto spectra.

astro-ph.CO↗

arXiv2026

Towards an optimal extraction of cosmological parameters from galaxy cluster surveys using convolutional neural networks

The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets. The latter need to be both realistic, as to reproduce the key features of the real data, and produced in large numbers, as to allow us to refine the precision of the training process. The analysis presented in this paper is an attempt to respond to these needs by (a) using clusters of galaxies as tracers of large-scale structure, together with (b) adopting a 3LPT code (Pinocchio) to generate a large training set of $32\,768$ mock X-ray cluster catalogues. X-ray luminosities are stochastically assigned to dark matter haloes using an empirical $M-L_X$ scaling relation. Using this training set, we test the ability and performances of a 3D convolutional neural network (CNN) to predict the cosmological parameters, based on an input overdensity field derived from the cluster distribution. We perform a comparison with a neural network trained on traditional summary statistics, that is, the abundance of clusters and their power spectrum. Our results show that the field-level analysis combined with the cluster abundance yields a mean absolute relative error on the predicted values of $Ω_{\rm m}$ and $σ_8$ that is a factor of $\sim 10 \%$ and $\sim 20\%$ better than that obtained from the summary statistics. Furthermore, when information about the individual luminosity of each cluster is passed to the CNN, the gain in precision exceeds $50\%$.

astro-ph.CO↗

arXiv2025

Disentangling Target Lines from Interlopers and Continuum with Neural Networks: A SPHEREx Intensity Mapping Case Study

Line-intensity mapping (LIM) traces the large-scale distribution of matter by measuring fluctuations in aggregate line emission from unresolved galaxies and the intergalactic medium, providing a powerful probe of both astrophysics and cosmology. However, interpreting LIM data is limited by our ability to disentangle the signal of a target spectral line from continuum emission and interloper lines, which are emissions from other redshifts that fall within the observed frequency band. Astrophysical modeling uncertainties further complicate matters, leaving the relative amplitudes of the map components poorly understood. In this paper, we present a neural-network (NN) approach to separate the three map components at the level of the angular power spectrum, explicitly accounting for uncertainties in their relative amplitudes. As test cases, we generate SPHEREx-like maps with variable interloper line luminosities across multiple frequency channels, with and without pixel-wise scatter and continuum contributions. We find that cross-channel correlations are essential for robust NN performance when scatter is present. The NN exhibits a hierarchy in residual errors: brighter components yield smaller residuals, and the dimmest the highest. Without continuum emission, the network recovers the target power spectrum to within $2.5\%$, while partially correcting the interloper spectra. With continuum included, the NN accurately reconstructs the power spectra of the continuum and target line, within $2\%$ and $6\%$, respectively, but fails to recover those of the interlopers. Reducing pixel-level scatter further improves performance, lowering residual errors to $1\%$ (continuum) and $3\%$ (target line).

astro-ph.CO↗

arXiv2025

Correcting for interloper contamination in the power spectrum with neural networks

Modern slitless spectroscopic surveys, such as Euclid and the Roman Space Telescope, collect vast numbers of galaxy spectra but suffer from low signal-to-noise ratios. This often leads to incorrect redshift assignments when relying on a single emission line, due to noise spikes or contamination from non-target emission lines, commonly referred to as redshift interlopers. We propose a machine learning approach to correct the impact of interlopers at the level of measured summary statistics, focusing on the power spectrum monopole and line interlopers as a proof of concept. To model interloper effects, we use halo catalogs from the Quijote simulations as proxies for galaxies, displacing a fraction of halos by the distance corresponding to the redshift offset between target and interloper galaxies. This yields contaminated catalogs with varying interloper fractions across a wide range of cosmologies from the Quijote suite. We train a neural network on the power spectrum monopole, alone or combined with the bispectrum monopole, from contaminated mocks to estimate the interloper fraction and reconstruct the cleaned power spectrum. We evaluate performance in two settings: one with fixed cosmology and another where cosmological parameters vary under broad priors. In the fixed case, the network recovers the interloper fraction and corrects the power spectrum to better than 1% accuracy. When cosmology varies, performance degrades, but adding bispectrum information significantly improves results, reducing the interloper fraction error by 40-60%. We also study the method's performance as a function of the size of the training set and find that optimal strategies depend on the correlation between target and interloper samples: bispectrum information aids performance when target and interloper galaxies are uncorrelated, while tighter priors are more effective when the two are strongly correlated.

astro-ph.CO↗

arXiv2025

Bispectrum constraints on Primordial non-Gaussianities with the eBOSS DR16 quasars

We present constraints on $f_{\rm NL}$, the parameter quantifying the amplitude of local Primordial Non-Gaussianities (PNG), from a combined analysis of the tree-level power spectrum and bispectrum of Data Release $16$ (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) quasar sample. In our analysis, we use the power spectrum measured with the optimal redshift weights that maximize the local PNG information together with the bispectrum estimated with the standard Feldman-Kaiser-Peacock weights. In the modeling, we incorporate the global and radial integral constraint corrections both in the power spectrum and in the bispectrum, for which we observe that only the radial integral constraint correction has a significant impact. Our constraints read $-6 < f_{\rm NL} < 20$ at $68\%$ confidence level and improve by $\sim 16\%$ over the previous power spectrum analysis of the same dataset. We observe the same improvement over the power spectrum analysis when the quasar response to PNG is lower. In this case, we find $-23 < f_{\rm NL} < 14$ at $68\%$ confidence level. Our findings are consistent with the Fisher matrix expectations.

astro-ph.CO↗

arXiv2023

Optimal constraints on Primordial non-Gaussianity with the eBOSS DR16 quasars in Fourier space

We present constraints on the amplitude of local Primordial Non-Gaussianities (PNG), $f_{\rm NL}$, using the quasar sample in the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 16. We analyze the power spectrum monopole, testing for the presence of scale dependent galaxy bias induced by local PNG. Our analysis makes use of optimal redshift weights that maximize the response of the quasar sample to the possible presence of non zero PNG. We find $-4<f_{\rm NL}<27$ at $68\%$ confidence level, which is among the strongest bounds with Large Scale Structure data. The optimal analysis reduces the error bar by about 10% compared to the standard one, an amount which is smaller than what was expected from a Fisher matrix analysis. This, and the reduced improvement over previous releases of the same catalog, suggest the presence of still unknown systematic effects in the data. If the quasars have a lower response to local PNG, our optimal constraint becomes $-23<f_{\rm NL}<21$ at $68\%$, with an improvement of $30\%$ over standard analyses. We also show how to use the optimal weights to put data-driven priors on the sample's response to local PNG.

astro-ph.CO↗