My track record
Download myCurriculum Vitae (updated at September 2024)
Check mySAO/NASA ADS complete list of pubblications
Check my ORCiD0000-0002-8106-2742
Impact of baryon physics in galaxy-galaxy strong lensing (GGSL) tension
Meneghetti et al. (2020) highlighted a tension between ΛCDM simulations and GGSL observations. InRagagnin et al. (2022a), we tested the well-motivated hypothesis that AGN physics could resolve this issue and we concluded that it can not.No current ΛCDM simulation reproduces the observed cores of galaxy clusters. While investigating this problem, I found that this tension is closely linked to the fact that most hydrodynamicsimulations tend to overestimate BCG masses.
Resources:youtube presentation recording
What characterises X-ray faint galaxy clusters?
It is well known that X-ray observations may be biased toward peaked and centrally concentrated gas distributions, potentially missing a population of X-ray faint objects. InRagagnin et al. (2022b),I found that clusters that are X-ray faint are gas-poor because they are old and relaxed, thus allowing sufficient time for the gas to be depleted.
Self-interacting dark matter (SIDM) in galaxy clusters
This promising particle-physics-motivated dark matter model can strongly influence the evolution of halos. InRagagnin et al. (2024), I present one of the first simulation suites (based on Dianoga ICs; see Rasia et al. 2014) of galaxy clusters incorporating full baryon physics and SIDM.We found that SIDM can significantly affect the abundance of satellites.A modular, GPU-friendly code for n-body simulations: hotwheels
I am developing a flexible and modular new implementation of a Gadget-like code (temporary project name:hotwheels), incorporating over a decade of experience working with HPC. It is designed to leveragemulticore and GPU architectures, with a strong emphasis onmodularity and miniapps, which are essential for collaboration with HPC facilities and GPU vendors.
Follow the progress on Mastodon:astrodon.social/@HotWheelsSims
Check the repository:www.ict.inaf.it/gitlab/hotwheels
Mock multi-wave length observations for Euclid
I am the lead author of a paper for theEuclid Collaboration: Ragagnin et al. (2025). Within this collaborative effort between observational and simulation teams, I produced mock observations from various multi-wavelength data sources(X-ray, SZ, optical, and inferred weak lensing signals). The paper presents a Euclid-tailored covariance matrix for realistic mock observations across these wavelengths and explores the impact of projection effects, which will assistfuture cosmology studies based on multi-wavelength observations.A web portal for cosmological hydrodynamic simulations
Thec2papcosmosim.uc.lrz.de web portal to allows to explore Magneticum simulation results and submit mock-observation jobs through a dedicated computing queue. It features a user-friendly graphical interface to navigate large simulated volumes, perform complex queries on galaxy cluster catalogs, and execute specific post-processing tasks (seeRagagnin et al. 2017).Seec2pap_batch.py for sending batch script.
Press coverage:Science Daily,Gauss Centre.
g3read library
Thegithub.com/aragagnin/g3read repository contains a collection ofPython tools for reading and post-processing Gadget2/3 snapshots, particularly for Magneticum outputs (including the INFO block), and efficiently handling FoF/SubFind catalogs. This repository includes core routines (g3read.py) that combine elements of Pynbody and ported legacy (Klaus') IDL routines.The library utilizes the GadgetFile class from Pynbody. If available, g3read will also leverage Numba for optimized performance.hydro_mc library
The packagehydro_mc implements theRagagnin et al. (2021)conversion of mass, concentration, and sparsity parameters between virial and overdensities of Δ = 2500, 500, and 200 (both mean and critical). See thehydro_mc web app or the repogithub.com/aragagnin/hydro_mc.
GPU porting of gravity and hydrodynamics
I ported the integration of various Gadget physics integrators (gravity, SPH density, SPH hydrodynamics, and thermal conduction) on GPUs. This was a significant collaborative effort, involving multiple hackathons and long-term partnerships with CSCS, PGI, and NVIDIA (see my CV). For further details, see my paper:Ragagnin et al. (2020).
Resources:youtube presentation recording