Particle Physics Seminar

Current and planned neutrino experiments address fundamental questions
in the neutrino, astrophysical, nuclear, and new physics sectors with
ambitious, large-scale facilities and detectors. Maximizing the
sensitivity and physics reach of these experiments is the guiding
principle for the design of the apparatus as well as the analysis
techniques applied to infer results from the data. These experiments,
however, pose challenges in this process: the data frequently have
ambiguities and some quantities are not measurable, such as the
momenta of outgoing neutrinos or recoiling nuclei. Detectors with
high density and spatial granularity provide a large number of
measured values for each event that must be sifted through to obtain
even basic reconstructed quantities. The impact of the values of
model parameters on the predicted event rates is not linear but is
frequently oscillatory. Systematic uncertainties must be highly
constrained in order to tease out small effects. To address these
challenges, a variety of sophisticated techniques have been adapted
from earlier experiments, such as well-established statistical methods
and analysis techniques. New, innovative tools developed in other
fields, such as deep-learning methods, are being applied to neutrino
experiments. I will give a survey of some of the interesting
developments being applied and planned for the future.