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pro vyhledávání: '"Schwan P"'
Reconfigurable electromagnetic structures (REMSs), such as reconfigurable reflectarrays (RRAs) or reconfigurable intelligent surfaces (RISs), hold significant potential to improve wireless communication and sensing systems. Even though several REMS m
Externí odkaz:
http://arxiv.org/abs/2411.13475
This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling st
Externí odkaz:
http://arxiv.org/abs/2409.13334
Measuring vector-boson scattering beyond the fully-leptonic final state is becoming possible at the LHC, which demands to have a solid control on the theory predictions for all final states of this class of processes. In this work we present a full o
Externí odkaz:
http://arxiv.org/abs/2406.12301
We report on a recent calculation of next-to-leading-order (NLO) QCD and electroweak corrections to like-sign W-boson scattering at the Large Hadron Collider, including all partonic channels and W-boson decays in the process $pp \to e^+ \nu_e \mu^+ \
Externí odkaz:
http://arxiv.org/abs/2405.18286
We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam b
Externí odkaz:
http://arxiv.org/abs/2405.09405
Autor:
The NNPDF Collaboration, Ball, Richard D., Barontini, Andrea, Candido, Alessandro, Carrazza, Stefano, Cruz-Martinez, Juan, Del Debbio, Luigi, Forte, Stefano, Giani, Tommaso, Hekhorn, Felix, Kassabov, Zahari, Laurenti, Niccolò, Magni, Giacomo, Nocera, Emanuele R., Rabemananjara, Tanjona R., Rojo, Juan, Schwan, Christopher, Stegeman, Roy, Ubiali, Maria
We extend the existing leading (LO), next-to-leading (NLO), and next-to-next-to-leading order (NNLO) NNPDF4.0 sets of parton distribution functions (PDFs) to approximate next-to-next-to-next-to-leading order (aN$^3$LO). We construct an approximation
Externí odkaz:
http://arxiv.org/abs/2402.18635
Autor:
The NNPDF Collaboration, Ball, Richard D., Barontini, Andrea, Candido, Alessandro, Carrazza, Stefano, Cruz-Martinez, Juan, Del Debbio, Luigi, Forte, Stefano, Giani, Tommaso, Hekhorn, Felix, Kassabov, Zahari, Laurenti, Niccolò, Magni, Giacomo, Nocera, Emanuele R., Rabemananjara, Tanjona R., Rojo, Juan, Schwan, Christopher, Stegeman, Roy, Ubiali, Maria
We include uncertainties due to missing higher order corrections to QCD computations (MHOU) used in the determination of parton distributions (PDFs) in the recent NNPDF4.0 set of PDFs. We use our previously published methodology, based on the treatme
Externí odkaz:
http://arxiv.org/abs/2401.10319
Autor:
The NNPDF Collaboration, Ball, Richard D., Barontini, Andrea, Candido, Alessandro, Carrazza, Stefano, Cruz-Martinez, Juan, Del Debbio, Luigi, Forte, Stefano, Giani, Tommaso, Hekhorn, Felix, Kassabov, Zahari, Laurenti, Niccolò, Magni, Giacomo, Nocera, Emanuele R., Rabemananjara, Tanjona R., Rojo, Juan, Schwan, Christopher, Stegeman, Roy, Ubiali, Maria
We construct a set of parton distribution functions (PDFs), based on the recent NNPDF4.0 PDF set, that also include a photon PDF. The photon PDF is constructed using the LuxQED formalism, while QED evolution accounting for O(alpha), O(alpha alphas) a
Externí odkaz:
http://arxiv.org/abs/2401.08749
Like-Sign W-Boson Scattering at the LHC -- Approximations and Full Next-to-Leading-Order Predictions
Publikováno v:
JHEP 11 (2023) 022
We present a new calculation of next-to-leading-order corrections of the strong and electroweak interactions to like-sign W-boson scattering at the Large Hadron Collider, implemented in the Monte Carlo integrator Bonsay. The calculation includes lept
Externí odkaz:
http://arxiv.org/abs/2308.16716
Autor:
Nghiem, Truong X., Drgoňa, Ján, Jones, Colin, Nagy, Zoltan, Schwan, Roland, Dey, Biswadip, Chakrabarty, Ankush, Di Cairano, Stefano, Paulson, Joel A., Carron, Andrea, Zeilinger, Melanie N., Cortez, Wenceslao Shaw, Vrabie, Draguna L.
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As oppos
Externí odkaz:
http://arxiv.org/abs/2306.13867