Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC.

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6   6   6 m liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light.

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration.

The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM).

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.

Using hypothesis testing on the mass-transfer effect with sulfate removal as an application.

Sulfate and chemical oxygen demand removal by an anaerobic batch reactor treating of acid mine drainage is modelled. Available models are abundant but excessively complex, with large numbers of free parameters. Here, maximum likelihood and chi-squared hypothesis tests are applied to three simple models, with Malthus-Monod and Fick description, in increasing order of complexity: (i) planktonic single population (of sulfate-reducing bacteria); (ii) planktonic double-competing populations (adding fermentative bacteria); (iii) granule bound double-competing populations.