Cosmic Ray Background Removal With Deep Neural Networks in SBND

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

Development of very-thick transparent GEMs with wavelength-shifting capability for noble element TPCs

A new concept for the simultaneous detection of primary and secondary scintillation in time projection chambers is proposed. Its core element is a type of very-thick GEM structure supplied with transparent electrodes and machined from a polyethylene naphthalate plate, a natural wavelength shifter. Such a device has good prospects for scalability and, by virtue of its genuine optical properties, it can improve on the light collection efficiency, energy threshold and resolution of conventional micropattern gas detectors. This, together with the intrinsic radiopurity of its constituting elements, offers advantages for noble gas and liquid based time projection chambers, used for dark matter searches and neutrino experiments. Production, optical and electrical characterization, and first measurements performed with the new device are reported.

Neural-network-driven proton decay sensitivity in the p → $$ \overline{\nu} $$K+ channel using large liquid argon time projection chambers

We report on an updated sensitivity for proton decay via p → $$ \overline{\nu} $$ ν ¯ K+ at large, dual phase liquid argon time projection chambers (LAr TPCs). Our work builds on a previous study in which several nucleon decay modes have been simulated and analyzed [1]. At the time several assumptions were needed to be made on the detector and the backgrounds. Since then, the community has made progress in defining these, and the computing power available enables us to fully simulate and reconstruct large samples in order to perform a better estimate of the sensitivity to proton decay. In this work, we examine the benchmark channel p → $$ \overline{\nu} $$ ν ¯ K+, which was previously found to be one of the cleanest channels. Using an improved neutrino event generator and a fully simulated LAr TPC detector response combined with a dedicated neural network for kaon identification, we demonstrate that a lifetime sensitivity of τ /Br (p → $$ \overline{\nu} $$ ν ¯ K+) > 7 × 1034 years at 90% confidence level can be reached at an exposure of 1 megaton · year in quasi-background-free conditions, confirming the superiority of the LAr TPC over other technologies to address the challenging proton decay modes.

Review of Liquid Argon Detector Technologies in the Neutrino Sector

Liquid Argon (LAr) is one of the most widely used scintillators in particle detection, due to its low cost, high availability and excellent scintillation properties. A large number of experiments in the neutrino sector are based around using LAr in one or more Time Projection Chambers (TPCs), leading to high resolution three-dimensional particle reconstruction. In this paper, we review and summarise a number of these Liquid Argon Time Projection Chamber (LArTPC) experiments, and briefly describe the specific technologies that they currently employ. This includes single phase LAr experiments (ICARUS T600, MicroBooNE, SBND, LArIAT, DUNE-SP, ProtoDUNE-SP, ArgonCube and Vertical Drift) and dual phase LAr experiments (DUNE-DP, WA105, ProtoDUNE-DP and ARIADNE). We also discuss some new avenues of research in the field of LArTPC readout, which show potential for wide-scale use in the near future.