Abstract
In detecting neutrinos from the Large Hadron Collider, FASER$\nu$ will record the most energetic laboratory neutrinos ever studied. While charged current neutrino scattering events can be cleanly identified by an energetic lepton exiting the interaction vertex, neutral current interactions are more difficult to detect. We explore the potential of FASER$\nu$ to observe neutrino neutral current scattering $\nu N \to \nu N$, demonstrating techniques to discriminate neutrino scattering events from neutral hadron backgrounds as well as to estimate the incoming neutrino energy given the deep inelastic scattering final state. We find that deep neural networks trained on kinematic observables allow for the measurement of the neutral current scattering cross section over neutrino energies from 100 GeV to several TeV. Such a measurement can be interpreted as a probe of neutrino non-standard interactions that is complementary to limits from other tests such as oscillations and coherent neutrino-nucleus scattering.
Highlights
As the only neutral and uncolored fermions in the Standard Model (SM), neutrinos are perhaps some of the least well understood particles in nature
We find that FASERν will be able to measure the neutrino neutral current (NC) cross section as a function of neutrino energy
While the fraction of the neutrino energy that is transferred to the nucleus has an almost uniform distribution, we find that the visible hadronic energy still serves as an excellent proxy for the energy of the incoming neutrino
Summary
As the only neutral and uncolored fermions in the Standard Model (SM), neutrinos are perhaps some of the least well understood particles in nature. One of the most basic measurements involving neutrinos is the scattering cross section of neutrinos off matter. In FASERν, there are significant backgrounds to NC scattering from neutral hadron interactions within the detector. Neutrino scattering and neutral hadron events at FASERν and use a neural network to effectively separate signal from background using kinematic information of the final state. By applying another neural network, we show that the energy of the neutrino can be estimated with ∼50% uncertainty.
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