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Hyperparameter Optimisation in Deep Learning from Ensemble Methods: Applications to Proton Structure
/ Cruz-Martinez, Juan (CERN) ; Jansen, Aaron (Netherlands eScience Center) ; van Oord, Gijs (Netherlands eScience Center) ; Rabemananjara, Tanjona R. (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Rocha, Carlos M.R. (Netherlands eScience Center) ; Rojo, Juan (CERN ; Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Stegeman, Roy (U. Edinburgh, Higgs Ctr. Theor. Phys.)
Deep learning models are defined in terms of a large number of hyperparameters, such as network architectures and optimiser settings. [...]
CERN-TH-2024-168 ; arXiv:2410.16248.
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An FONLL prescription with coexisting flavor number PDFs
/ Barontini, Andrea (INFN, Milan ; Milan U.) ; Candido, Alessandro (INFN, Milan ; Milan U. ; CERN) ; Hekhorn, Felix (INFN, Milan ; Milan U. ; Jyvaskyla U. ; Helsinki U.) ; Magni, Giacomo (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Stegeman, Roy (U. Edinburgh, Higgs Ctr. Theor. Phys.)
We present a new prescription to account for heavy quark mass effects in the determination of parton distribution functions (PDFs) based on the FONLL scheme. Our prescription makes explicit use of the freedom to choose the number of active flavors at a given scale and, thus, use coexisting PDFs with different active flavor number. [...]
arXiv:2408.07383; Nikhef-2024-014; Edinburgh 2024/5; TIF-UNIMI-2024-13.-
2024-10-01 - 17 p.
- Published in : JHEP 2410 (2024) 004
Fulltext: document - PDF; 2408.07383 - PDF;
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The path to $\hbox {N}^3\hbox {LO}$ parton distributions
/ NNPDF Collaboration
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 to the N$^3$LO splitting functions that includes all available partial information from both fixed-order computations and from small and large $x$ resummation, and estimate the uncertainty on this approximation by varying the set of basis functions used to construct the approximation. [...]
arXiv:2402.18635; Nikhef-2023-020; TIF-UNIMI-2023-23; Edinburgh 2023/29.-
2024-07-03 - 59 p.
- Published in : Eur. Phys. J. C 84 (2024) 659
Fulltext: 2402.18635 - PDF; Publication - PDF;
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Yadism: Yet Another Deep-Inelastic Scattering Module
/ Candido, Alessandro (INFN, Milan ; Milan U. ; CERN) ; Hekhorn, Felix (INFN, Milan ; Milan U. ; Jyvaskyla U. ; Helsinki U.) ; Magni, Giacomo (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Rabemananjara, Tanjona R. (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Stegeman, Roy (U. Edinburgh, Higgs Ctr. Theor. Phys.)
We present yadism, a library for the evaluation of both polarized and unpolarized deep-inelastic scattering (DIS) structure functions and cross sections up to N$^3$LO in perturbative QCD. The package provides computations of observables in fixed-flavor and zero-mass variable flavor number schemes. [...]
arXiv:2401.15187; CERN-TH-2024-015; Edinburgh 2024/4; Nikhef-2024-002.-
2024-07-15 - 28 p.
- Published in : Eur. Phys. J. C 84 (2024) 697
Fulltext: document - PDF; 2401.15187 - PDF;
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Photons in the proton: implications for the LHC
/ NNPDF Collaboration
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) and O(alpha^2) corrections is implemented and benchmarked by means of the EKO code. [...]
arXiv:2401.08749; TIF-UNIMI-2023-17; Edinburgh 2023/19; CERN-TH-2023-159.-
2024-05-28 - 32 p.
- Published in : Eur. Phys. J. C 84 (2024) 540
Fulltext: document - PDF; 2401.08749 - PDF;
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The intrinsic charm quark valence distribution of the proton
/ Ball, Richard D. (U. Edinburgh, Higgs Ctr. Theor. Phys.) ; Candido, Alessandro (INFN, Milan ; Milan U. ; CERN) ; Cruz-Martinez, Juan (CERN) ; Forte, Stefano (INFN, Milan ; Milan U.) ; Giani, Tommaso (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Hekhorn, Felix (INFN, Milan ; Milan U. ; Jyvaskyla U. ; Helsinki U.) ; Magni, Giacomo (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Nocera, Emanuele R. (Turin U.) ; Rojo, Juan (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Stegeman, Roy (U. Edinburgh, Higgs Ctr. Theor. Phys.)
/NNPDF Collaboration
We provide a first quantitative indication that the wave function of the proton contains unequal distributions of charm quarks and antiquarks, i.e. a nonvanishing intrinsic valence charm distribution. [...]
arXiv:2311.00743; Edinburgh 2023/22; TIF-UNIMI-2023-28; Nikhef 2023-012,
CERN-TH-2023-196.-
2024-05-01 - 11 p.
- Published in : Phys. Rev. D 109 (2024) L091501
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Response to "Parton distributions need representative sampling"
/ Ball, Richard D. (U. Edinburgh, Higgs Ctr. Theor. Phys.) ; Cruz-Martinez, Juan (CERN) ; Del Debbio, Luigi (U. Edinburgh, Higgs Ctr. Theor. Phys.) ; Forte, Stefano (INFN, Milan ; Milan U.) ; Kassabov, Zahari (Cambridge U., DAMTP) ; Nocera, Emanuele R. (Turin U.) ; Rojo, Juan (Vrije U., Amsterdam ; Nikhef, Amsterdam) ; Stegeman, Roy (U. Edinburgh, Higgs Ctr. Theor. Phys.) ; Ubiali, Maria (Cambridge U., DAMTP)
We respond to the criticism raised by Courtoy et al., in which the faithfulness of the NNPDF4.0 sampling is questioned and an under-estimate of the NNPDF4.0 PDF uncertainties is implied. [...]
arXiv:2211.12961 ; Edinburgh 2022/19 ; TIF-UNIMI-2022-21.
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Response to - Fulltext
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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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