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New Deep Learning Data Analysis Method for PROSPECT using GAPE: Genetic Algorithm Powered Evolution
M. Adriamirado, A. B. Balantekin, C. Bass +40
We propose a genetic algorithm powered evolution (GAPE) method to create deep learning solutions for energy and position estimation for reactor antineutrino interactions in the Pre…
DUNE Software and Computing Research and Development
DUNE Collaboration, A. Abed Abud, R. Acciarri +1340
The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase s…
Machine Learning for Single-Ended Event Reconstruction in PROSPECT Experiment
M. Andriamirado, A. B. Balantekin, C. D. Bass +43
The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge,…
Bayesian Analysis of a Future Beta Decay Experiment's Sensitivity to Neutrino Mass Scale and Ordering
A. Ashtari Esfahani, M. Betancourt, Z. Bogorad +43
Bayesian modeling techniques enable sensitivity analyses that incorporate detailed expectations regarding future experiments. A model-based approach also allows one to evaluate inf…