Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classical and Machine Learning Methods for Event Reconstruction in
  NeuLAND   NeuLAND, the New Large Area Neutron Detector, is a key component to
investigate the origin of matter in the universe with experimental nuclear
physics. It is a core component of the Reactions with Relativistic Radioactive
Beams setup at the Facility for Antiproton and Ion Research, Germany. Neutrons
emitted from these reactions create a wide range of patterns in NeuLAND. From
these patterns, the number of neutrons (multiplicity) and their first
interaction points must be reconstructed to determine the neutrons'
four-momenta. In this paper, we detail the challenges involved in this
reconstruction and present a range of possible solutions. Scikit-Learn
classification models and simple Keras-based neural networks were trained on a
wide range of input-scaler combinations and compared to classical models. While
the improvement in multiplicity reconstruction is limited due to the overlap
between features, the machine learning methods achieve a significantly better
first interaction point selection, which directly improves the resolution of
physical quantities.

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The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
1
The categories of this paper are: cs
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: econ
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph