Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Physics Validation of Novel Convolutional 2D Architectures for Speeding
  Up High Energy Physics Simulations   The precise simulation of particle transport through detectors remains a key
element for the successful interpretation of high energy physics results.
However, Monte Carlo based simulation is extremely demanding in terms of
computing resources. This challenge motivates investigations of faster,
alternative approaches for replacing the standard Monte Carlo approach.
  We apply Generative Adversarial Networks (GANs), a deep learning technique,
to replace the calorimeter detector simulations and speeding up the simulation
time by orders of magnitude. We follow a previous approach which used
three-dimensional convolutional neural networks and develop new two-dimensional
convolutional networks to solve the same 3D image generation problem faster.
Additionally, we increased the number of parameters and the neural networks
representational power, obtaining a higher accuracy. We compare our best
convolutional 2D neural network architecture and evaluate it versus the
previous 3D architecture and Geant4 data. Our results demonstrate a high
physics accuracy and further consolidate the use of GANs for fast detector
simulations.

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg