| Records |
| Author |
Candido, A.; Garcia, A.; Magni, G.; Rabemananjara, T.; Rojo, J.; Stegeman, R. |
| Title |
Neutrino structure functions from GeV to EeV energies |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
05 |
Issue |
5 |
Pages |
149 - 78pp |
| Keywords |
Deep Inelastic Scattering or Small-x Physics; Neutrino Interactions; Parton Distributions |
| Abstract |
The interpretation of present and future neutrino experiments requires accurate theoretical predictions for neutrino-nucleus scattering rates. Neutrino structure functions can be reliably evaluated in the deep-inelastic scattering regime within the perturbative QCD (pQCD) framework. At low momentum transfers (Q(2) less than or similar to few GeV2), inelastic structure functions are however affected by large uncertainties which distort event rate predictions for neutrino energies E-nu up to the TeV scale. Here we present a determination of neutrino inelastic structure functions valid for the complete range of energies relevant for phenomenology, from the GeV region entering oscillation analyses to the multi-EeV region accessible at neutrino telescopes. Our NNSF nu approach combines a machine-learning parametrisation of experimental data with pQCD calculations based on state-of-the-art analyses of proton and nuclear parton distributions (PDFs). We compare our determination to other calculations, in particular to the popular Bodek-Yang model. We provide updated predictions for inclusive cross sections for a range of energies and target nuclei, including those relevant for LHC far-forward neutrino experiments such as FASER nu, SND@LHC, and the Forward Physics Facility. The NNSF nu determination is made available as fast interpolation LHAPDF grids, and it can be accessed both through an independent driver code and directly interfaced to neutrino event generators such as GENIE. |
| Address |
[Candido, Alessandro] Univ Milan, Dipartimento Fis, Tif Lab, Via Celoria 16, I-20133 Milan, Italy, Email: alessandro.candido@mi.infn.it; |
| Corporate Author |
|
Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1029-8479 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000992767300011 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5559 |
| Permanent link to this record |
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| Author |
Costantini, M.N.; Mantani, L.; Moore, J.M.; Ubiali, M. |
| Title |
A linear PDF model for Bayesian inference |
Type |
Journal Article |
| Year |
2026 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
04 |
Issue |
4 |
Pages |
068 - 34pp |
| Keywords |
Deep Inelastic Scattering or Small-x Physics; Parton Distributions |
| Abstract |
A robust uncertainty estimate in global analyses of Parton Distribution Functions (PDFs) is essential at the Large Hadron Collider (LHC), especially in view of the high-precision data anticipated by experimentalists in the High-Luminosity phase of the LHC. A Bayesian framework to determine PDFs provides a rigorous treatment of uncertainties and full control on the prior, though its practical implementation can be computationally demanding. To address these challenges, we introduce a novel approach to PDF determination tailored for Bayesian inference, based on the use of linear models. Unlike traditional parametrisations, our method represents PDFs as vectors in a functional space spanned by specially chosen bases, derived from the dimensional reduction of a neural network functional space, providing a compact yet versatile representation of PDFs. The low-dimensionality of the preferred models allows for particularly fast inference. The size of the bases can be systematically adjusted, allowing for transparent control over underfitting and overfitting, and facilitating principled model selection through Bayesian workflows. In this work, the methodology is applied to a fit of Deep Inelastic Scattering synthetic data, and thoroughly tested via multi-closure tests, thus paving the way to its application to global PDF fits. |
| Address |
[Costantini, Mark N.; Ubiali, Maria] Univ Cambridge, DAMTP, Wilberforce Rd, Cambridge CB3 0WA, England, Email: mnc33@cam.ac.uk; |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:001737731600005 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
7174 |
| Permanent link to this record |
