Giles Barr

Abstract:

This paper reports on the search for heavy neutrinos with masses in the range $140 < M_N < 493$ MeV/c$^2$ using the off-axis near detector ND280 of the T2K experiment. These particles can be produced from kaon decays in the standard neutrino beam and then subsequently decay in ND280. The decay modes under consideration are $N \to \ell^{\pm}_{\alpha} \pi^{\mp}$ and $N \to \ell^+_{\alpha} \ell^-_{\beta} \nu (\bar\nu)$ ($\alpha,\beta=e,\mu$). A search for such events has been made using the Time Projection Chambers of ND280, where the background has been reduced to less than two events in the current dataset in all channels. No excess has been observed in the signal region. A combined Bayesian statistical approach has been applied to extract upper limits on the mixing elements of heavy neutrinos to electron-, muon- and tau- flavoured currents ($U_e^2$, $U_{\mu}^2$, $U_{\tau}^2$) as a function of the heavy neutrino mass, e.g. $U_e^2 < 10^{-9}$ at $90\%$ C.L. for a mass of $390$ MeV/c$^2$. These constraints are competitive with previous experiments.

Abstract:

All rights reserved. Neutrino neutral-current (NC) induced single photon production is a subleading order process for accelerator-based neutrino beam experiments including T2K. It is, however, an important process to understand because it is a background for electron (anti)neutrino appearance oscillation experiments. Here, we performed the first search of this process below 1 GeV using the finegrained detector at the T2K ND280 off-axis near detector. By reconstructing single photon kinematics from electron positron pairs, we achieved 95% pure gamma ray sample from 5.738 1020 protons-on-targets neutrino mode data. We do not find positive evidence of NC induced single photon production in this sample. We set the model-dependent upper limit on the cross-section for this process, at 0.114 10-38 cm2 (90% C.L.) per nucleon, using the J-PARC off-axis neutrino beam with an average energy of .En. ~ 0.6 GeV. This is the first limit on this process below 1.GeV which is important for current and future oscillation experiments looking for electron neutrino appearance oscillation signals.

Abstract:

We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a νμ charged-current neutral pion data samples.