Abstract
In this contribution, we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account for the dependence on the atomic mass number A, and introduce novel algorithms to improve the training of the neural network parameters within the NNPDF framework. Finally, we present preliminary results of an nPDF fit to neutral current deep-inelastic lepton–nucleus scattering data, and demonstrate how one can validate the new fitting methodology by means of closure tests.
| Original language | English |
|---|---|
| Pages (from-to) | 927-933 |
| Number of pages | 7 |
| Journal | Acta Physica Polonica B, Proceedings Supplement |
| Volume | 12 |
| Issue number | 4 |
| Early online date | 14 Nov 2018 |
| DOIs | |
| Publication status | Published - 2019 |
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Keywords
- hep-ph
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Nuclear parton distributions from neural networks. / Khalek, Rabah Abdul; Ethier, Jacob J.; Rojo, Juan.
In: Acta Physica Polonica B, Proceedings Supplement, Vol. 12, No. 4, 2019, p. 927-933.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Nuclear parton distributions from neural networks
AU - Khalek, Rabah Abdul
AU - Ethier, Jacob J.
AU - Rojo, Juan
PY - 2019
Y1 - 2019
N2 - In this contribution, we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account for the dependence on the atomic mass number A, and introduce novel algorithms to improve the training of the neural network parameters within the NNPDF framework. Finally, we present preliminary results of an nPDF fit to neutral current deep-inelastic lepton–nucleus scattering data, and demonstrate how one can validate the new fitting methodology by means of closure tests.
AB - In this contribution, we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account for the dependence on the atomic mass number A, and introduce novel algorithms to improve the training of the neural network parameters within the NNPDF framework. Finally, we present preliminary results of an nPDF fit to neutral current deep-inelastic lepton–nucleus scattering data, and demonstrate how one can validate the new fitting methodology by means of closure tests.
KW - hep-ph
UR - http://www.scopus.com/inward/record.url?scp=85075515698&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075515698&partnerID=8YFLogxK
U2 - 10.5506/APhysPolBSupp.12.927
DO - 10.5506/APhysPolBSupp.12.927
M3 - Article
VL - 12
SP - 927
EP - 933
JO - Acta Physica Polonica B, Proceedings Supplement
JF - Acta Physica Polonica B, Proceedings Supplement
SN - 1899-2358
IS - 4
ER -