Speakers
Description
The detection of TeV-energy neutrinos by the LHC far-forward detectors FASER/FASER$\nu$ and SND@LHC enables novel opportunities to validate theoretical predictions of of forward light particle production. In this work we present work in progress towards using the FASER and FPF event rate measurements to extract the LHC forward neutrino fluxes in a theory-agnostic manner by means of machine learning techniques. We parametrise these neutrino fluxes with neural networks and train them to available and projected FASER data, differential in neutrino energy and pseudo-rapidity. This way one can estimate the expected precision for this determination of the LHC neutrino fluxes and use this information to constrain models of forward $D$-meson production. This approach aims to demonstrate that FASER/FPF neutrino measurements provide an efficient data-driven approach to calibrate the prompt neutrino fluxes at neutrino telescopes such as IceCube and KM3NET.
