arXiv subjects

N. Frontiere

Publications and source records attributed to N. Frontiere.

4 recordsLinked to original sources

arXiv2026

Optimization of Weak Lensing Lightcone Simulations for Higher-Order Statistics in the LSST era

We present a framework for generating lightcone simulations tailored to the analysis of Stage-IV cosmic shear data using Higher-Order Statistics (HOS). We revisit key design choices from previous simulation campaigns and re-optimize several internal parameters, benchmarking accuracy through changes in $\chi^2$ of cosmic shear statistics under survey conditions mimicking 10 years of observations from the Legacy Survey of Space and Time (LSST). We find that discretizing the lightcone uniformly in scale factor yields higher accuracy than commonly adopted schemes such as uniform spacing in redshift or comoving distance. While $N_{\rm part} = 1024^3$ simulation particles (corresponding to a mass resolution of $m_{\rm part} = 2.08\times10^{10}M_\odot$) is sufficient to model two-point statistics up to $\ell = 5000$, we observed significant instabilities on our full suite of HOS as the number of mass shells used in the lightcone construction, $N_{\rm shells}$, is varied. In contrast, simulations with $N_{\rm part} = 2048^3$ particles ($m_{\rm part} = 2.60\times10^{9}M_\odot$) robustly reproduce all statistics considered. In this higher-resolution configuration, $N_{\rm shells}$ can be reduced to $\sim50$ with only minor deviations, no larger than $0.1-0.3\sigma$ relative to our highest-resolution case ($N_{\rm shells}\sim100$). This has been explicitly verified through a comparison between our fiducial lightcone production mode based on slicing particle snapshots and an exact lightcone mode where individual particle trajectories are solved for at runtime. We further show that the particle density per pixel can be downsampled by a significant amount for $z>1.5$, saving large computational resources with no impact on the resulting statistics. These results guide the design of upcoming simulation campaigns geared towards forward-modeling and emulation-based analyses of Stage-IV data.

astro-ph.CO↗

arXiv2024

The picasso gas model: Painting intracluster gas on gravity-only simulations

We introduce picasso, a model designed to predict thermodynamic properties of the intracluster medium based on the properties of halos in gravity-only simulations. The predictions result from the combination of an analytical gas model, mapping gas properties to the gravitational potential, and of a machine learning model to predict the model parameters for individual halos based on their scalar properties, such as mass and concentration. Once trained, the model can be applied to make predictions for arbitrary potential distributions, allowing its use with flexible inputs such as N-body particle distributions or radial profiles. We present the model, and train it using pairs of gravity-only and hydrodynamic simulations. We show that when trained to learn the mapping from gravity-only to non-radiative hydrodynamic simulations, picasso can make remarkably accurate and precise predictions of intracluster gas thermodynamics, with percent-level bias and $\sim 20 \%$ scatter for $r / R_{500c} \in [0.1,1]$. Training the model on hydrodynamic simulations including sub-resolution physics modeling yields robust predictions as well, albeit with the introduction of a radius-dependent bias and an increase in scatter. We further show that the model can be trained to make accurate predictions from very minimal halo information, down to mass and concentration, at the cost of modestly reduced precision. picasso is made publicly available as a Python package at https://github.com/fkeruzore/picasso, which includes trained models that can be used to make predictions easily and efficiently, in a fully auto-differentiable and hardware-accelerated framework

astro-ph.CO↗

arXiv2023

Optimization and Quality Assessment of Baryon Pasting for Intracluster Gas using the Borg Cube Simulation

Synthetic datasets generated from large-volume gravity-only simulations are an important tool in the calibration of cosmological analyses. Their creation often requires accurate inference of baryonic observables from the dark matter field. We explore the effectiveness of a baryon pasting algorithm in providing precise estimations of three-dimensional gas thermodynamic properties based on gravity-only simulations. We use the Borg Cube, a pair of simulations originating from identical initial conditions, with one run evolved as a gravity-only simulation, and the other incorporating non-radiative hydrodynamics. Matching halos in both simulations enables comparisons of gas properties on an individual halo basis. This comparative analysis allows us to fit for the model parameters that yield the closest agreement between the gas properties in both runs. To capture the redshift evolution of these parameters, we perform the analysis at five distinct redshift steps, spanning from $z=0$ to $2$. We find that the investigated algorithm, utilizing information solely from the gravity-only simulation, achieves few-percent accuracy in reproducing the median intracluster gas pressure and density, albeit with a scatter of approximately 20%, for cluster-scale objects up to $z=2$. We measure the scaling relation between integrated Compton parameter and cluster mass ($Y_{500c} | M_{500c}$), and find that the imprecision of baryon pasting adds less than 5% to the intrinsic scatter measured in the hydrodynamic simulation. We provide best-fitting values and their redshift evolution, and discuss future investigations that will be undertaken to extend this work.

astro-ph.CO↗

arXiv2016

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.

physics.comp-ph↗