Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of a Convolutional Neural Network for image classification
  to the analysis of collisions in High Energy Physics   The application of deep learning techniques using convolutional neural
networks to the classification of particle collisions in High Energy Physics is
explored. An intuitive approach to transform physical variables, like momenta
of particles and jets, into a single image that captures the relevant
information, is proposed. The idea is tested using a well known deep learning
framework on a simulation dataset, including leptonic ttbar events and the
corresponding background at 7 TeV from the CMS experiment at LHC, available as
Open Data. This initial test shows competitive results when compared to more
classical approaches, like those using feedforward neural networks.

12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph