Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying the Quantum Properties of Hadronic Resonances using Machine
  Learning   With the great promise of deep learning, discoveries of new particles at the
Large Hadron Collider (LHC) may be imminent. Following the discovery of a new
Beyond the Standard model particle in an all-hadronic channel, deep learning
can also be used to identify its quantum numbers. Convolutional neural networks
(CNNs) using jet-images can significantly improve upon existing techniques to
identify the quantum chromodynamic (QCD) (`color') as well as the spin of a
two-prong resonance using its substructure. Additionally, jet-images are useful
in determining what information in the jet radiation pattern is useful for
classification, which could inspire future taggers. These techniques improve
the categorization of new particles and are an important addition to the
growing jet substructure toolkit, for searches and measurements at the LHC now
and in the future.

32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas