arXiv subjects

Giulia Degni

Publications and source records attributed to Giulia Degni.

5 recordsLinked to original sources

arXiv2026

The Back-in-time Void Finder: dynamical identification of cosmic voids through optimal transport reconstruction

Cosmic voids have increasingly emerged as a powerful cosmological probe. However, their large spatial extent and intrinsically underdense environments make their identification highly sensitive to shot noise, redshift-space distortions (RSD), and observational systematics, particularly for topological and density-based void definitions. We introduce the Back-In-Time Void Finder (BitVF), a novel dynamical and physically motivated algorithm that identifies cosmic voids as regions of negative divergence of the Lagrangian displacement field reconstructed from the present-day tracer distribution. The reconstruction relies on an optimized discrete optimal transport algorithm that recovers the backward-in-time dynamics of tracers, naturally accounting for tracer bias without relying on cosmological assumptions. We validate BitVF against the widely used topological void finder REVOLVER using high-resolution N-body simulations, showing that it produces void catalogs with smoother and more physically motivated density profiles, as well as abundances that are more stable under tracer subsampling and shot noise. We further apply it to realistic DESI-like mock light-cone galaxy catalogs, demonstrating that it intrinsically mitigates redshift-space systematic effects, preserving real-space void size functions more faithfully than topological methods. Modeling RSD, the reconstruction can be combined with a fiducial cosmology and an assumed tracer bias within a bias-corrected Kaiser framework, yielding reconstructed-space void catalogs consistent with real-space statistics across redshift. Its performance is characterized as a function of the main internal parameters, showing an optimal balance between accuracy, computational efficiency, and applicability to stage IV galaxy surveys. BitVF will be publicly released within the CosmoBolognaLib.

astro-ph.CO↗

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.

astro-ph.CO↗

arXiv2026

Kinematic lensing with high-resolution spectroscopic surveys. A unique opportunity for transformative cosmology at high redshifts in the 2040s

We present a science case to perform high-redshift cosmic shear surveys for cosmology with next-generation spectroscopic instruments, such as the proposed MegaMapper and Wide-field Spectroscopic Telescope. We argue that by using the novel technique called 'kinematic lensing' (KL) it will be possible to obtain shear catalogues at redshifts between 2 and 5. We show that the signal-to-noise ratio of KL at such high redshifts is on average twice as much that expected from current weak lensing (WL) surveys such as Euclid or LSST, and several times that of the previous generation of WL surveys like DES and KiDS, even with very conservative assumptions about the fraction of spectroscopically-detected sources for which KL shear estimates will be available. This will allow cosmologists to perform joint galaxy clustering-cosmic shear analyses over unprecedented cosmic volumes and to probe the growth of structures deep in the matter-dominated era and across the onset of dark-energy domination, offering a unique opportunity to unveil the mystery of cosmic acceleration.

astro-ph.CO↗

arXiv2025

From Redshift to Real Space: Combining Linear Theory With Neural Networks

Spectroscopic redshift surveys are key tools to trace the large-scale structure (LSS) of the Universe and test the $\Lambda$CDM model. However, using redshifts as distance proxies introduces distortions in the 3D galaxy distribution. If uncorrected, these distortions lead to systematic errors in LSS analyses and cosmological parameter estimation. We present a new method that combines linear theory (LT) and a neural network (NN) to mitigate redshift space distortions (RSDs). The hybrid LT+NN approach is trained and validated on dark matter halo fields from z = 1 snapshots of the Quijote N-body simulations. LT corrects large-scale distortions in the linear regime, while the NN learns quasi-linear and small-scale features. The LT correction is applied first, then the NN is trained on the resulting fields to improve accuracy across scales. The method uses a Mean Squared Error (MSE) loss and yields significant performance gains: approximately 50% improvement over LT alone and 12% over NN alone. The reconstructed fields from the LT+NN method show stronger correlations with the true real-space fields than either LT or NN separately. The hybrid method also improves clustering statistics such as halo-halo and halo-void correlations, with benefits extending to BAO scales. Compared to NN-only, it provides better suppression of spurious anisotropies on large and quasi-linear scales, as measured by the quadrupole moments of correlation functions. This work shows that combining a physically motivated dynamical model with a machine learning algorithm leverages the strengths of both approaches. The LT+NN method achieves high accuracy with modest training data and computational cost, making it a promising tool for future applications to more realistic galaxy surveys.

astro-ph.CO↗

arXiv2024

Cosmology with voids from the Nancy Grace Roman Space Telescope

We provide an accurate forecast of the expected constraining power from the main void statistics -- the void size function and the void-galaxy cross-correlation function -- to be measured by the Roman reference High Latitude Spectroscopic Survey from the Nancy Grace Roman Space Telescope. Relying on a realistic galaxy mock lightcone, covering 2000 square degrees, we find more than $8\times 10^4 $ voids and explore their constraining power in the framework of three different cosmological models: $\Lambda$CDM, $w$CDM, and $w_0 w_{\rm a}$CDM. This work confirms the strong complementarity of different void statistics and showcases the constraining power to be expected from Roman voids thanks to the combination of its high tracer density and large observed volume.

astro-ph.CO↗