arXiv subjects

Andrea Fiorilli

Publications and source records attributed to Andrea Fiorilli.

5 recordsLinked to original sources

arXiv2026

Aletheia: Emulating the halo mass function with evolution mapping

We present an emulator of the halo mass function (HMF) based on Gaussian-process regression, that exploits the evolution mapping framework. This framework splits cosmological parameters into shape parameters ($\mathbf{\Theta}_\mathrm{s}$), which set the shape of the linear power spectrum, and evolution parameters ($\mathbf{\Theta}_\mathrm{e}$), which only rescale its amplitude, with their net effect entirely captured by the clustering amplitude, $\sigma_{12}$. The emulation acts in two steps: first, an emulator predicts the overall shape of the HMF given $\mathbf{\Theta}_\mathrm{s}$ and $\sigma_{12}$; then, a second emulator applies a small correction accounting for the residual dependence on the integrated growth history of density fluctuations. The emulator is trained on a suite of 100 simulations of flat $\Lambda$CDM cosmologies but, owing to evolution mapping, can be applied to predict the abundance of haloes in cosmologies with any extended space of $\mathbf{\Theta}_\mathrm{e}$, such as curvature, quintessence and dynamical dark energy. We test the emulator against independent $\Lambda$CDM runs and on an additional validation suite of dynamical dark energy simulations, achieving per cent-level accuracy over a wide range of masses and redshifts, with the performance only slightly degrading at early times, where the training data points are fewer. Compared to other state-of-the-art HMF emulators, its accuracy is higher in nearly all tested cases. Our results demonstrate the broad applicability of the evolution mapping framework to build very accurate emulators of statistics of the density field. The emulator is publicly available as part of the Aletheia emulator suite.

astro-ph.CO↗

arXiv2026

CNN+FoF: application of deep learning to the identification of dark matter haloes

We present a deep-learning-based approach for identifying dark matter haloes in cosmological N-body simulations. Our framework consists of a volumetric Convolutional Neural Network to classify individual simulation particles as either halo or non-halo members, followed by a highly optimised and parallelised Friends-of-Friends clustering algorithm that groups the classified halo members into distinct haloes. The training data comprise simulations generated using GADGET-4, with labels obtained with the ROCKSTAR halo finder. Our models incorporate two main halo mass definitions, $M_{200\mathrm{b}}$ and $M_{\text{vir}}$, with similar performance. For haloes defined by the ROCKSTAR $M_{200\mathrm{b}}$ criterion, the classification network demonstrated stable performance across multiple simulation resolutions. For the highest resolution, it achieved over $98\%$ across all primary performance metrics when identifying halo particles. Furthermore, the FoF algorithm yielded halo catalogues with a purity generally exceeding $95\%$ and a stable completeness of $93\%$ for masses above $5\times10^{11} \, M_\odot$. Our pipeline recovered the centre-of-mass positions, velocities and halo masses with high fidelity, yielding a halo mass function consistent to within $5\%$ of the reference while faithfully reconstructing the internal density profiles. The primary objective of this study is to offer a faster and scalable alternative to conventional halo finders, achieving a speed-up of approximately one order of magnitude relative to ROCKSTAR, offering a promising pathway for modern simulation-based inference methods that rely on rapid and accurate structure identification.

astro-ph.CO↗

arXiv2025

Evolution mapping III: A new recipe for the halo mass function

We present a new prescription for the halo mass function (HMF) built upon the Evolution Mapping framework. This approach provides a physical motivation to parametrise the non-universality of the HMF in terms of the recent history of structure formation and the local shape of the linear matter power spectrum. Our model was calibrated against measurements from N-body simulations, with halo samples defined by ten overdensity thresholds, $\Delta$, ranging from 150 to 1600 times the mean background matter density. For our reference mass definition, $\Delta=200$, the calibrated fitting function achieves per cent-level accuracy across a wide range of masses, redshifts, and structure formation histories, and maintains this performance when tested on cosmologies with different linear power spectrum shapes. This high level of accuracy is maintained across other mass definitions, degrading only slightly to the 5 per cent level at the highest values of $\Delta$. We also provide fitting formulae to interpolate the parameters as a function of $\Delta$, which allows for accurate modelling of HMFs defined by intermediate overdensities, with accuracy still well within 5 per cent when tested on halo catalogues defined by the virial overdensity threshold. Compared to other commonly used recipes, our prescription yields competitive or superior accuracy across all redshifts and cosmologies, successfully capturing the non-universal features of the HMF where other models exhibit systematic deviations. This work provides a high-precision modelling tool for cluster abundance analyses, and demonstrates the power of the evolution mapping framework for building accurate models of observables in the non-linear regime.

astro-ph.CO↗

arXiv2025

Aletheia: Emulating the non-linear matter power spectrum in the context of evolution mapping

We present Aletheia, a new emulator of the non-linear matter power spectrum, $P(k)$, built upon the evolution mapping framework. This framework addresses the limitations of traditional emulation by focusing on $h$-independent cosmological parameters, which can be separated into those defining the linear power spectrum shape ($\mathbf{\Theta}_{\mathrm{s}}$) and those affecting only its amplitude evolution ($\mathbf{\Theta}_{\mathrm{e}}$). The combined impact of evolution parameters and redshift is compressed into a single amplitude parameter, $\sigma_{12}$. Aletheia uses a two-stage Gaussian Process emulation: a primary emulator predicts the non-linear boost factor as a function of ($\mathbf{\Theta}_{\mathrm{s}}$) and $\sigma_{12}$ for fixed evolution parameters, while a second one applies a small linear correction based on the integrated growth history. The emulator is trained on shape parameters spanning $\pm$5$\sigma$ of Planck constraints and a wide clustering range $0.2 < \sigma_{12} < 1.0$, providing predictions for $0.006\,{\rm Mpc}^{-1} < k < 2\,{\rm Mpc}^{-1}$. We validate Aletheia against N-body simulations, demonstrating sub-percent accuracy. When tested on a suite of dynamic dark energy models, the full emulator's predictions show a variance of approximately 0.2%, a factor of five smaller than that of the state-of-the-art EuclidEmulator2 (around 1% variance). Furthermore, Aletheia maintains sub-percent accuracy for the best-fit dynamic dark energy cosmology from recent DESI data, a model whose parameters lie outside the training ranges of most conventional emulators. This demonstrates the power of the evolution mapping approach, providing a robust and extensible tool for precision cosmology.

astro-ph.CO↗

arXiv2024

A revisited Correction to the Halo Mass Function for local-type Primordial non-Gaussianity

We investigate the effect of primordial non-Gaussianities on halo number counts using N-body simulations with different values of $f_{\rm NL}^{\rm loc}$. We show how current theoretical models fail to adequately describe the non-Gaussian mass function of halos identified with different overdensity thresholds, $\Delta_{\rm b}$. We explain how these discrepancies are related to a variation in the density profile of dark matter halos, finding that the internal steepness (i.e. the compactness) of halos depends on the value of $f_{\rm NL}^{\rm loc}$. We then parametrize these deviations in halo number counts with a factor $\kappa(\Delta_{\rm b})$ that modifies the linear density threshold for collapse according to the halo identification threshold used, defined with respect to the Universe background density. We rely on a second-degree polynomial to describe $\kappa$ and employ a Bayesian analysis to determine the coefficients of this polynomial. In addition, we verify the independence of the latter on the sign and absolute value of $f_{\rm NL}^{\rm loc}$. Finally, we show how this re-parametrization prevents the extraction of biased constraints on $f_{\rm NL}^{\rm loc}$, correcting for large systematic errors especially in the case of halos identified with high density thresholds. This improvement is crucial in the perspective of deriving cosmological constraints with the non-Gaussian mass function from real data, as different mass definitions can be employed depending on the properties of the survey.

astro-ph.CO↗