Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised learning of photoelectron counting in scintillator-based dark
  matter experiments   Many scintillator based detectors employ a set of photomultiplier tubes (PMT)
to observe the scintillation light from potential signal and background events.
It is important to be able to count the number of photoelectrons (PE) in the
pulses observed in the PMTs, because the position and energy reconstruction of
the events is directly related to how well the spatial distribution of the PEs
in the PMTs as well as their total number might be measured. This task is
challenging for fast scintillators, since the PEs often overlap each other in
time. Standard Bayesian statistics methods are often used and this has been the
method employed in analyzing the data from liquid argon experiments such as
MiniCLEAN and DEAP. In this work, we show that for the MiniCLEAN detector it is
possible to use a multi-layer perceptron to learn the number of PEs using only
raw pulse features with better accuracy and precision than existing methods.
This can even help to perform position reconstruction with better accuracy and
precision, at least in some generic cases.

5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph