Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Deep Learning Techniques to Search for the MiniBooNE Low Energy
  Excess in MicroBooNE with > 3$\sigma$ Sensitivity   This thesis describes an analysis developed for the MicroBooNE experiment to
investigate an anomalous excess of electron-like events observed in the
MiniBooNE detector. The hypothesis investigated here is that the MiniBooNE
anomaly represents appearance of electron neutrinos. Using an amalgam of novel
Deep Learning and standard algorithmic techniques this analysis reconstructs
and identifies a highly pure sample of charged current quasi-elastic muon
neutrino and electron neutrino interactions. This thesis describes the steps in
the analysis chain and provides data-to-simulation comparisons for each step
that establish confidence in the final prediction. When interpreted in the
context of a $\nu e$ appearance like model, this analysis predicts a
3.2$\sigma$ sensitivity to exclude a standard model fluctuation which would
appear as a MiniBooNE like anomaly using $7\times10^{20}$ protons on target of
MicroBooNE Data.

23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
1
The categories of this paper are: cs
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat