NNSFν is a python module that provides predictions for neutrino structure functions. It relies on YADISM for the large-Q region while the low-Q regime is modelled in terms of a Neural Network (NN). The NNSFν determination is also made available in terms of fast interpolation LHAPDF grids that can be accessed through an independent driver code and directly interfaced to the GENIE Monte Carlo neutrino event generators.
To refer to NNSFν in a scientific publication, please use the following:
@article{Candido:2023utz,
author = "Candido, Alessandro and Garcia, Alfonso and Magni, Giacomo and Rabemananjara, Tanjona and Rojo, Juan and Stegeman, Roy",
title = "{Neutrino Structure Functions from GeV to EeV Energies}",
eprint = "2302.08527",
archivePrefix = "arXiv",
primaryClass = "hep-ph",
reportNumber = "Nikhef 2022-014, Edinburgh 2022/27, TIF-UNIMI-2023-5",
month = "2",
year = "2023"
}And if NNSFν proved to be useful in your work, consider also to reference the codes:
@misc{https://doi.org/10.5281/zenodo.7657132,
doi = {10.5281/ZENODO.7657132},
url = {https://zenodo.org/record/7657132},
author = "Candido, Alessandro and Garcia, Alfonso and Magni, Giacomo and Rabemananjara, Tanjona and Rojo, Juan and Stegeman, Roy",
title = "{Neutrino Structure Functions from GeV to EeV Energies}",
publisher = {Zenodo},
year = {2023},
copyright = {Open Access}
}