Villaescusa-Navarro, Francisco - Resultados da pesquisa

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Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine learning and Simulations / Rose, Jonah C. (Florida U. ; New York U., CCPP) ; Torrey, Paul (Virginia U.) ; Villaescusa-Navarro, Francisco (New York U., CCPP ; Princeton U., Astrophys. Sci. Dept.) ; Lisanti, Mariangela (New York U., CCPP ; Princeton U.) ; Nguyen, Tri (MIT, MKI ; IAIFI, Cambridge) ; Roy, Sandip (Princeton U.) ; Kollmann, Kassidy E. (Princeton U.) ; Vogelsberger, Mark (MIT, MKI ; IAIFI, Cambridge) ; Cyr-Racine, Francis-Yan (New Mexico U.) ; Medvedev, Mikhail V. (Princeton U., Astrophys. Sci. Dept. ; Princeton, Inst. Advanced Study ; Harvard-Smithsonian Ctr. Astrophys. ; Kansas U. ; MIT, LNS) et al.
We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. [...]
arXiv:2405.00766. - 28 p.
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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning / Shanahan, Phiala (MIT) ; Terao, Kazuhiro (SLAC) ; Whiteson, Daniel (UC, Irvine) ; Aarts, Gert (Swansea U. ; ECT, Trento ; Fond. Bruno Kessler, Trento) ; Adelmann, Andreas (Northeastern U. ; PSI, Villigen) ; Akchurin, N. (Texas Tech.) ; Alexandru, Andrei (George Washington U. ; Maryland U.) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Apresyan, Artur (Fermilab) et al.
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. [...]
arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD. -
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BE-HaPPY: Bias Emulator for Halo Power Spectrum including massive neutrinos / Valcin, David (ICC, Barcelona U.) ; Villaescusa-Navarro, Francisco (Flatiron Inst., New York) ; Verde, Licia (ICC, Barcelona U. ; ICREA, Barcelona) ; Raccanelli, Alvise (ICC, Barcelona U. ; CERN)
We study the clustering properties of dark matter halos in real- and redshift-space in cosmologies with massless and massive neutrinos through a large set of state-of-the-art N-body simulations. We provide quick and easy-to-use prescriptions for the halo bias on linear and mildly non-linear scales, both in real and redshift space, which are valid also for massive neutrinos cosmologies. [...]
arXiv:1901.06045; CERN-TH-2019-005.- 2019-12-18 - 38 p. - Published in : JCAP 12 (2019) 057 Fulltext: PDF;

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Accurate initial conditions in mixed dark matter–baryon simulations / Valkenburg, Wessel (CERN) ; Villaescusa-Navarro, Francisco (Trieste Observ. ; INFN, Trieste)
We quantify the error in the results of mixed baryon--dark-matter hydrodynamic simulations, stemming from outdated approximations for the generation of initial conditions. The error at redshift 0 in contemporary large simulations, is of the order of few to ten percent in the power spectra of baryons and dark matter, and their combined total-matter power spectrum. [...]
arXiv:1610.08501; CERN-TH-2016-226.- 2017-06-01 - 9 p. - Published in : Mon. Not. R. Astron. Soc. 467 (2017) 4401-4409 Fulltext: PDF; Oxford University Press Open Access article: PDF; External link: Preprint

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2	Villaescusa-Navarro, F
1	Villaescusa-Navarro, F.

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