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 T...

Full description

Saved in:
Bibliographic Details
Published inThe European physical journal. C, Particles and fields Vol. 81; no. 7; pp. 1 - 11
Main Authors Maevskiy, A., Ratnikov, F., Zinchenko, A., Riabov, V.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2021
Springer
Springer Nature B.V
SpringerOpen
Subjects
Analysis
Astronomy
Astrophysics and Cosmology
Chambers
Charged particles
Computing time
Detectors
Elementary Particles
Generative adversarial networks
Hadrons
Heavy Ions
Measurement Science and Instrumentation
Nuclear Energy
Nuclear Physics
Particle tracking
Physics
Physics and Astronomy
Population distribution
Prototypes
Quantum Field Theories
Quantum Field Theory
Radiation counters
Regular Article - Experimental Physics
Sensors
Simulation
String Theory
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1434-6044
1434-6052
DOI:10.1140/epjc/s10052-021-09366-4