Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simulating the Time Projection Chamber responses at the MPD detector
  using Generative Adversarial Networks   High energy physics experiments rely heavily on the detailed detector
simulation models in many tasks. Running these detailed models typically
requires a notable amount of the computing time available to the experiments.
In this work, we demonstrate a new approach to speed up the simulation of the
Time Projection Chamber tracker of the MPD experiment at the NICA accelerator
complex. Our method is based on a Generative Adversarial Network - a deep
learning technique allowing for implicit estimation of the population
distribution for a given set of objects. This approach lets us learn and then
sample from the distribution of raw detector responses, conditioned on the
parameters of the charged particle tracks. To evaluate the quality of the
proposed model, we integrate a prototype into the MPD software stack and
demonstrate that it produces high-quality events similar to the detailed
simulator, with a speed-up of at least an order of magnitude. The prototype is
trained on the responses from the inner part of the detector and, once expanded
to the full detector, should be ready for use in physics tasks.

14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg