prikaz prve stranice dokumenta Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
  Download
PDF 978.04 KB
Scientific paper - Original scientific paper
Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
Entropy, 22 (2020), 9; 994. https://doi.org/10.3390/e22090994
Jerčić, Marko; Poljak, Nikola

Cite this document

Jerčić, M. & Poljak, N. (2020). Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model. Entropy, 22. (9). doi: 10.3390/e22090994

Jerčić, Marko and Nikola Poljak. "Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model." Entropy, vol. 22, no. 9, 2020. https://doi.org/10.3390/e22090994

Jerčić, Marko and Nikola Poljak. "Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model." Entropy 22, no. 9 (2020). https://doi.org/10.3390/e22090994

Jerčić, M. and Poljak, N. (2020) 'Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model', Entropy, 22(9). doi: 10.3390/e22090994

Jerčić M, Poljak N. Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model. Entropy [Internet]. 2020 September 07 [cited 2024 December 24];22(9). doi: 10.3390/e22090994

M. Jerčić and N. Poljak, "Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model", Entropy, vol. 22, no. 9, September 2020. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:217:420392. [Accessed: 24 December 2024]

Please login to the repository to save this object to your list.
Metadata
Title (english)Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
AuthorMarko Jerčić
AuthorNikola Poljak
Author's institutionUniversity of Zagreb
Faculty of Science
(Department of Physics)
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract (english)
The application of machine learning methods to particle physics often does not provide enough understanding of the underlying physics. An interpretable model which provides a way to improve our knowledge of the mechanism governing a physical system directly from the data can be very useful. In this paper, we introduce a simple artificial physical generator based on the Quantum chromodynamical (QCD) fragmentation process. The data simulated from the generator are then passed to a neural network model which we base only on the partial knowledge of the generator. We aimed to see if the interpretation of the generated data can provide the probability distributions of basic processes of such a physical system. This way, some of the information we omitted from the network model on purpose is recovered. We believe this approach can be beneficial in the analysis of real QCD processes.
Keywords (croatian)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleEntropy
Numberingvol. 22, no. 9, 994
p-ISSN1099-4300
DOIhttps://doi.org/10.3390/e22090994
URN:NBNurn:nbn:hr:217:420392
Publication2020-09-07
Document URLhttps://www.mdpi.com/1099-4300/22/9/994
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Public note© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Received: 26 July 2020; accepted: 3 September 2020; published: 7 September 2020.
Created on2020-11-20 15:07:00