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CERN Document Server 11 registres trobats  1 - 10següent  anar al registre: La cerca s'ha fet en 1.26 segons. 
1.
Accelerating Berends–Giele recursion for gluons in arbitrary dimensions over finite fields / Cruz-Martinez, Juan M. (CERN) ; De Laurentis, Giuseppe (U. Edinburgh, Higgs Ctr. Theor. Phys.) ; Pellen, Mathieu (Freiburg U.)
This work provides a proof of concept for the computation of pure gluonic amplitudes in quantum chromodynamics (QCD) on graphics processing units (GPUs). The implementation relies on the Berends-Giele recursion algorithm and, for the first time on a GPU, enables the numerical computation of amplitudes in an arbitrary number of space-time dimensions and over finite fields. [...]
arXiv:2502.07060; CERN-TH-2025-017; FR-PHENO-2025-001.- 2025-05-29 - 16 p. - Published in : Eur. Phys. J. C 85 (2025) 590 Fulltext: 2502.07060 - PDF; document - PDF;
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Physics with Muons at the FPF / Trojanowski, Sebastian (speaker) ; Cruz Martinez, Juan M. (speaker) (CERN) ; Kling, Felix (speaker) (DESY) ; Sandrock, Alexander (speaker)
2023 - 3945. Conferences, Workshops & Schools; Forward Physics Facility Theory Workshop External links: Talk details; Event details In : Forward Physics Facility Theory Workshop
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3.
The LHC as a Neutrino-Ion Collider / Cruz-Martinez, Juan M. (CERN) ; Fieg, Max (UC, Irvine) ; Giani, Tommaso (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Krack, Peter (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Mäkelä, Toni (NCBJ, Warsaw) ; Rabemananjara, Tanjona R. (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Rojo, Juan (Vrije U., Amsterdam ; NIKHEF, Amsterdam)
Proton-proton collisions at the LHC generate a high-intensity collimated beam of neutrinos in the forward (beam) direction, characterised by energies of up to several TeV. The recent observation of LHC neutrinos by FASER$\nu$ and SND@LHC signals that this hitherto ignored particle beam is now available for scientific inquiry. [...]
arXiv:2309.09581; Nikhef-2023-009; CERN-TH-2023-165.- 2024-04-08 - 42 p. - Published in : Eur. Phys. J. C 84 (2024) 369 Fulltext: 2309.09581 - PDF; document - PDF;
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Multi-variable integration with a variational quantum circuit / Cruz-Martinez, Juan M. (CERN) ; Robbiati, Matteo (CERN ; Milan U. ; INFN, Milan) ; Carrazza, Stefano (CERN ; Milan U. ; INFN, Milan ; Technol. Innovation Inst., UAE)
In this work we present a novel strategy to evaluate multi-variable integrals with quantum circuits. The procedure first encodes the integration variables into a parametric circuit. [...]
arXiv:2308.05657; TIF-UNIMI-2023-13; CERN-TH-2023-157.- 2024-06-25 - 12 p. - Published in : Quantum Sci. Technol. 9 (2024) 035053 Fulltext: 2308.05657 - PDF; document - PDF;
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Theory prediction in PDF fitting / Barontini, Andrea (Milan U. ; INFN, Milan) ; Candido, Alessandro (Milan U. ; INFN, Milan) ; Cruz-Martinez, Juan M. (CERN) ; Hekhorn, Felix (Milan U. ; INFN, Milan) ; Schwan, Christopher (Wurzburg U.)
Continuously comparing theory predictions to experimental data is a common task in analysis of particle physics such as fitting parton distribution functions (PDFs). [...]
arXiv:2303.07119. - 5 p.
Fulltext
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Determining probability density functions with adiabatic quantum computing / Robbiati, Matteo (CERN ; Milan U. ; INFN, Milan) ; Cruz-Martinez, Juan M. (CERN) ; Carrazza, Stefano (CERN ; Milan U. ; INFN, Milan ; Technol. Innovation Inst., UAE)
The two main approaches to quantum computing are gate-based computation and analog computation, which are polynomially equivalent in terms of complexity, and they are often seen as alternatives to each other. In this work, we present a method for fitting one-dimensional probability distributions as a practical example of how analog and gate-based computation can be used together to perform different tasks within a single algorithm. [...]
arXiv:2303.11346; TIF-UNIMI-2023-9; CERN-TH-2023-042.- 2025-01-04 - 11 p. - Published in : Quantum Machine Intelligence 7 (2025) 5 Fulltext: 2303.11346 - PDF; document - PDF;
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Pineline: Industrialization of High-Energy Theory Predictions / Barontini, Andrea (Milan U. ; INFN, Milan) ; Candido, Alessandro (Milan U. ; INFN, Milan) ; Cruz-Martinez, Juan M. (CERN) ; Hekhorn, Felix (Milan U. ; INFN, Milan) ; Schwan, Christopher (Wurzburg U.)
We present a collection of tools automating the efficient computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes often require dedicated programs. [...]
arXiv:2302.12124; TIF-UNIMI-2023-4; CERN-TH-2023-021.- 2023-12-19 - 8 p. - Published in : Comput. Phys. Commun. 297 (2024) 109061 Fulltext: 2302.12124 - PDF; Publication - PDF;
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A data-based parametrization of parton distribution functions / Carrazza, Stefano (Milan U. ; INFN, Milan ; CERN ; Technol. Innovation Inst., UAE ) ; Cruz-Martinez, Juan M. (Milan U. ; INFN, Milan) ; Stegeman, Roy (Milan U. ; INFN, Milan)
Since the first determination of a structure function many decades ago, all methodologies used to determine structure functions or parton distribution functions (PDFs) have employed a common prefactor as part of the parametrization. The NNPDF collaboration pioneered the use of neural networks to overcome the inherent bias of constraining the space of solution with a fixed functional form while still keeping the same common prefactor as a preprocessing. [...]
arXiv:2111.02954; TIF-UNIMI-2021-18.- 2022-02-22 - 10 p. - Published in : Eur. Phys. J. C 82 (2022) 163 Fulltext: 2111.02954 - PDF; document - PDF;
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MadFlow: towards the automation of Monte Carlo simulation on GPU for particle physics processes / Cruz Martínez, Juan M. (speaker) (University of Milan)
In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In order to automate MC simulation for a generic number of processes, we design a program which provides to the user the possibility to simulate custom processes through the MG5_aMC@NLO framework. [...]
2021 - 797. Conferences; 25th International Conference on Computing in High Energy & Nuclear Physics External links: Talk details; Event details In : 25th International Conference on Computing in High Energy & Nuclear Physics
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PDFFlow: Parton distribution functions on GPU / Carrazza, Stefano (INFN, Milan ; Milan U.) ; Cruz-Martinez, Juan M. (INFN, Milan ; Milan U.) ; Rossi, Marco (INFN, Milan ; CERN ; Milan U.)
We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. [...]
arXiv:2009.06635.- 2021-07 - 8 p. - Published in : Comput. Phys. Commun. 264 (2021) 107995 Fulltext: PDF; External link: GitHub
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