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CERN Document Server 3,435 записей найдено  1 - 10следующийконец  перейти к записи: Поиск длился 0.67 секунд. 
1.
Making Likelihood Calculations Fast: Automatic Differentiation Applied to RooFit / Singh, Garima (CERN ; Princeton U.) ; Rembser, Jonas (CERN) ; Moneta, Lorenzo (CERN) ; Lange, David (Princeton U.) ; Vassilev, Vassil (Princeton U.)
With the growing datasets of current and next-generation HighEnergy and Nuclear Physics (HEP/NP) experiments, statistical analysis has become more computationally demanding. These increasing demands elicit improvements and modernizations in existing statistical analysis software. [...]
2024 - 7 p. - Published in : EPJ Web Conf. 295 (2024) 06014 Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.06014
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2.
Search for Inelastic Boosted Dark Matter with the ICARUS Detector at the Gran Sasso Underground National Laboratory / ICARUS Collaboration
We present the result of a search for inelastic boosted dark matter using the data corresponding to an exposure of 0.13 kton$\cdot$year, collected by the ICARUS T-600 detector during its 2012--2013 operational period at the INFN Gran Sasso Underground National Laboratory. [...]
arXiv:2412.09516 ; FERMILAB-PUB-24-0873 ; University of Texas at Arlington Ph.D. Thesis. - 16.
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3.
First implementation and results of the Analysis Grand Challenge with a fully Pythonic RDataFrame / Padulano, Vincenzo Eduardo (CERN) ; Guiraud, Enrico (CERN ; Princeton U.) ; Falko, Andrii (Taras Shevchenko U.) ; Gazzarrini, Elena (CERN) ; Garcia Garcia, Enrique (CERN) ; Gosein, Domenic (CERN ; Mannheim U.)
The growing amount of data generated by the LHC requires a shift in how HEP analysis tasks are approached. Efforts to address this computational challenge have led to the rise of a middle-man software layer, a mixture of simple, effective APIs and fast execution engines underneath. [...]
2024 - 8 p. - Published in : EPJ Web Conf. 295 (2024) 06011 Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.06011
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4.
A novel liquid argon purity monitor based on $^{207}$Bi / Baibussinov, B. (INFN, Padua) ; Bettini, M. (INFN, Padua) ; Fabris, F. (INFN, Padua) ; Gan, R. ; Guglielmi, A. (INFN, Padua) ; Gurung, G. (CERN ; Texas U., Arlington) ; Marchini, S. (INFN, Padua) ; Meng, G. (INFN, Padua) ; Nicoletto, M. (INFN, Padua) ; Pietropaolo, F. (CERN) et al.
A novel liquid argon purity monitor based on a 207 Bi radioactive source, emitting monochromatic internal-conversion electrons, is presented. [...]
arXiv:2411.10796. - 11.
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5.
Study of ablation and shock generation across three orders of magnitude of laser intensity with 100 ps laser pulses / Parsons, S E (LLNL, Livermore ; UC, San Diego) ; Armstrong, M R (LLNL, Livermore) ; Lee, H J (SLAC) ; Gleason, A E (SLAC) ; Goncharov, A F (Carnegie Inst., Wash., D.C.) ; Belof, J (LLNL, Livermore) ; Prakapenka, V (Chicago U., EFI) ; Granados, E (CERN) ; Beg, F N (UC, San Diego) ; Radousky, H B (LLNL, Livermore)
The laser ablation and subsequent shock generation in solid targets plays an important role in a variety of research topics from equation of state models for materials to inertial confinement fusion. One of the long-standing issues is the knowledge of ablation depth in the picosecond time regime. [...]
2024 - 7 p. - Published in : Appl. Phys. Lett. 125 (2024) 164104
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6.
Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC / Mondal, Spandan (Brown U.) ; Mastrolorenzo, Luca (CERN)
The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review provides a detailed examination of current and past ML techniques in this domain. [...]
arXiv:2404.01071.- 2024-07-19 - 38 p. - Published in : Eur. Phys. J. Spec. Top.
Fulltext: PDF;
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Testing the Neutrino Content of the Muon at Muon Colliders / Capdevilla, Rodolfo (Fermilab) ; Garosi, Francesco (Garching, Max Planck Inst. ; SISSA, Trieste ; INFN, Trieste) ; Marzocca, David (INFN, Trieste) ; Stechauner, Bernd (CERN ; Vienna, Tech. U.)
Collinear emission of $W$ bosons off a high-energy muon induces a large muon-neutrino component among the Parton Distribution Functions (PDFs) of a muon. [...]
FERMILAB-PUB-24-0575-T ; arXiv:2410.21383. - 22.
Fermilab Library Server - Fulltext - Fulltext
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8.
Constraints on dark photon dark matter from Lyman-$\alpha$ forest simulations and an ultra-high signal-to-noise quasar spectrum / Trost, Andrea (Trieste U. ; Trieste Observ. ; INFN, Trieste) ; Bolton, James S. (Nottingham U.) ; Caputo, Andrea (CERN) ; Liu, Hongwan (Boston U. ; Chicago U., KICP ; Fermilab) ; Cristiani, Stefano (Trieste Observ. ; INFN, Trieste ; IFPU, Trieste) ; Viel, Matteo (Trieste Observ. ; INFN, Trieste ; IFPU, Trieste ; SISSA, Trieste)
The ultralight dark photon is a well-motivated, hypothetical dark matter candidate. [...]
arXiv:2410.02858 ; FERMILAB-PUB-24-0739-V. - 14.
Fermilab Library Server - Fulltext
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9.
Equivariant neural networks for robust <math display="inline"><mi>C</mi><mi>P</mi></math> observables / Cruz, Sergio Sánchez (CERN) ; Kolosova, Marina (U. Florida, Gainesville (main)) ; Ramón Álvarez, Clara (ICTEA, Oviedo) ; Petrucciani, Giovanni (CERN) ; Vischia, Pietro (ICTEA, Oviedo)
We introduce the usage of equivariant neural networks in the search for violations of the charge-parity ($\textit{CP}$) symmetry in particle interactions at the CERN Large Hadron Collider. We design neural networks that take as inputs kinematic information of recorded events and that transform equivariantly under the a symmetry group related to the $\textit{CP}$ transformation. [...]
arXiv:2405.13524.- 2024-11-01 - 10 p. - Published in : Phys. Rev. D Fulltext: Publication - PDF; 2405.13524 - PDF;
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10.
Geant4 electromagnetic physics for Run3 and Phase2 LHC / Hahnfeld, Jonas (CERN) ; Ivanchenko, Vladimir (CERN ; Princeton U.) ; Novak, Mihaly (CERN) ; Pandola, Luciano (INFN, Catania) ; Sawkey, Daren (Varian Assoc., Palo Alto)
For the new Geant4 series 11.X, the electromagnetic (EM) physics sub-libraries were revised and reorganized in view of requirements for simulation of Phase-2 LHC experiments. EM physics simulation takes a significant fraction of the available CPU during massive production of Monte Carlo events for LHC experiments. [...]
2024 - 4 p. - Published in : EPJ Web Conf. 295 (2024) 03018 Fulltext: PDF;
In : 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.03018
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