arXiv subjects

Gabriele Parimbelli

Publications and source records attributed to Gabriele Parimbelli.

7 recordsLinked to original sources

arXiv2026

cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era

cloelib is a Python library developed to compute cosmological observables within the Cosmology Likelihood for Observables in Euclid (CLOE) ecosystem (cloe-org). As cosmology enters a precision era driven by galaxy survey missions such as Euclid, there is a growing need for flexible, efficient, and differentiable software capable of supporting next-generation inference pipelines. cloelib addresses these demands through a modular architecture that interfaces seamlessly with established Boltzmann solvers whilst incorporating JAX-based automatic differentiation to enable gradient-based methods. The library defines consistent protocols for background evolution, perturbations, and non-linear structure formation, and supports a wide range of observables, including photometric and spectroscopic large-scale structure probes, as well as cross-correlations with the Cosmic Microwave Background and galaxy clusters. In its finalised form, cloelib is intended to serve as the reference theory computation infrastructure for Euclid's first cosmological release, bridging traditional numerical cosmology with modern optimisation techniques and emerging machine learning approaches to inference.

astro-ph.CO↗

arXiv2025

Halo Lightcones with Optimised Orientation and Interpolation in Cosmological Simulations -- an application to mock H$\alpha$ selected galaxies

A critical step to create realistic mock catalogs that support large-scale photometric and spectroscopic sky surveys is the production of cosmological simulations that accurately model the survey observables taking into account the redshift-dependent galaxy formation and evolution processes. Here we develop an efficient framework, HOLCon (Halo Optimised Lightcone Constructor), for post-facto construction of dark matter halo lightcones from simulations and use them to generate a mock galaxy catalogue. HOLCon includes a module to optimise the lightcone's orientation within the simulation box, minimising repeated structures when the survey volume exceeds a single box -- a common challenge in modern surveys. A linear interpolation scheme tracks the evolution of halo properties across snapshots. Applied to the publicly available Uchuu simulation, we construct a lightcone of 50 ${\rm deg}^2$ and extending up to $z = 10$, providing representative coverage of deep fields of Stage IV surveys. We validate the lightcone for cosmological applications by comparing the dark matter halo clustering in the lightcone with those from the original simulation snapshots. Subsequently, we make the galaxy-halo connection on the lightcone with a redshift extended version of the SciPIC algorithm producing a comprehensive set of descriptive galaxy attributes. HOLCon leverages Dask, a scalable parallel computing pythonic framework for fast construction of dark matter halo lightcones enabling rapid creation of multiple statistical realizations essential for robust cosmological inference. The produced galaxy mock makes predictions for clustering of H$\alpha$ emitters, making it a useful cosmology resource.

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arXiv2025

Modeling spatially-resolved galactic H$\alpha$ emission for galaxy clustering

Near-infrared spectroscopic surveys target high-redshift emission-line galaxies (ELGs) to probe cosmological scenarios. Understanding the clustering properties of ELGs is essential to derive optimal constraints. We present a simple radiative transfer model for spatially resolved galactic H$\alpha$ emission, which includes emission from the warm-hot diffuse interstellar medium. The atomic level populations are in steady-state and computed in the coronal approximation. The model is applied to multiple IllustrisTNG simulations in the redshift range $1\leq z \leq 2$ to produce the luminosity function (LF) and the halo occupation distribution (HOD). Collisional processes account for a significant fraction of $\approx 40\%$ of the total ${\rm H}\alpha$ luminosity ($L_{{\rm H}\alpha}$). Our LFs are in reasonable agreement with measurements from H$\alpha$ surveys if a uniform extinction of $0.3<A_{{\rm H}\alpha}<0.85$ mag is assumed. Our HOD is consistent with that of the ${\it Euclid}$ Flagship galaxy mock up to differences that can be attributed to baryonic feedback, which is absent from the latter. When H$\alpha$ luminosities are computed from an empirical relation between $L_{{\rm H}\alpha}$ and the total star formation rate (SFR) the resulting LFs are in tension with previous observations. Our approach can be extended to other atomic lines, which should be helpful for the mining of high-redshift galaxy spectra in forthcoming surveys.

astro-ph.GA↗

arXiv2024

The multi-dimensional halo assembly bias can be preserved when enhancing halo properties with HALOSCOPE

Over $90$\% of dark matter haloes in cosmological simulations have unresolved properties. This can hinder the dynamical range of simulations and result in systematic biases when modelling cosmological tracers. We aim to more precisely determine unresolved structural and dynamical halo properties while preserving the correlations with environment and halo assembly bias found in simulations. We have developed HALOSCOPE, a machine learning technique that uses multi-variate conditional probability distribution functions. This method ensures that correlations among various halo properties, as well as their dependence on the local environment, are preserved. In this work, we trained HALOSCOPE with a high-resolution (HR) simulation and used it to better determine the properties (concentration, spin, and two shape parameters) of unresolved dark matter haloes in an eight times lower resolution simulation. HALOSCOPE is able to recover the multi-dimensional halo assembly bias, that is, the correlations of different combinations of halo properties with the large-scale environment, measured in the HR simulation. This is achieved by including the linear halo-by-halo bias and tidal anisotropy in the set of input training parameters. HALOSCOPE, by design, also recovers the joint distribution of the halo properties. To study how resolution effects propagate into the clustering of model galaxies, we generated catalogues of central galaxies using two implementations of the assembly bias in a halo occupation distribution model. The clustering of central model galaxies is improved by a factor of three at $0.009<k ({\rm Mpc}^{-1}h)<0.6$ when the unresolved haloes are enhanced with HALOSCOPE. HALOSCOPE can improve the accuracy of cosmological tracer catalogues produced with approximate methods when many realisations are needed.

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arXiv2023

Constraining Hot Dark Matter Sub-Species with Weak Lensing and the Cosmic Microwave Background Radiation

Although it is well known that the bulk of dark matter (DM) has to be cold, the existence of an additional sub-dominant, hot species remains a valid possibility. In this paper we investigate the potential of the cosmic shear power spectrum to constrain such a mixed (hot plus cold) DM scenario with two additional free parameters, the hot-to-total DM fraction ($f_{\rm hdm}$) and the thermal mass of the hot component ($m_{\rm hdm}$). Running a Bayesian inference analysis for both the Kilo-Degree Survey cosmic shear data (KiDS) as well as the Cosmic Microwave Background (CMB) temperature and polarisation data from Planck, we derive new constraints for the mixed DM scenario. We find a 95 per cent confidence limit of $f_{\rm hdm}<0.08$ for a very hot species of $m_{\rm hdm}\leq20$ eV. This constraint is weakened to $f_{\rm hdm}<0.25$ for $m_{\rm hdm}\leq80$ eV. Scenarios with masses above $m_{\rm hdm}\sim200$ eV remain unconstrained by the data. Next to providing limits, we investigate the potential of mixed DM to address the clustering (or $S_8$) tension between lensing and the CMB. We find a reduction of the 2D ($\Omega_m - S_8$) tension from 2.9$\sigma$ to 1.6$\sigma$ when going from a pure cold DM to a mixed DM scenario. When computing the 1D gaussian tension on $S_8$ the improvement is milder, from 2.4$\sigma$ to 2.0$\sigma$.

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arXiv2022

The CAMELS project: public data release

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$α$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.

astro-ph.CO↗

arXiv2019

On the degeneracy between baryon feedback and massive neutrinos as probed by matter clustering and weak lensing

Massive neutrinos, due to their free streaming, produce a suppression in the matter power spectrum at intermediate and small scales which could be probed by galaxy clustering and/or weak lensing observables. This effect happens at scales that are also influenced by baryon feedback, i.e. galactic winds or Active Galactic Nuclei (AGN) feedback, which in realistic hydrodynamic simulations has also been shown to produce a suppression of power. Leaving aside, for the moment, the complex issue of galaxy bias, we focus here on matter clustering and tomographic weak lensing, we investigate the possible degeneracy between baryon feedback and neutrinos showing that it is not likely to degrade significantly the measurement of neutrino mass in future surveys. To do so, we generate mock data sets and fit them using the Markov Chain Monte Carlo (MCMC) technique and explore degeneracies between feedback parameters and neutrino mass. We model baryon feedback through fitting functions, while massive neutrinos are accounted for, also in the non-linear regime, using Halofit calibrated against accurate N-body neutrino simulations. In the error budget, we include the uncertainty in the modelling of non-linearities. For both matter clustering and weak lensing, we always recover the input neutrino mass within $\sim 0.25σ$ confidence level. Finally, we also take into account the intrinsic alignment effect in the weak lensing mock data. Even in this case, we are able to recover the right parameters: in particular, we find a significant degeneracy pattern between $M_ν$ and the intrinsic alignment parameter $A_\mathrm{IA}$ .

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