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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

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Jerčić, M. i 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 i Nikola Poljak. "Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model." Entropy, vol. 22, br. 9, 2020. https://doi.org/10.3390/e22090994

Jerčić, Marko i Nikola Poljak. "Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model." Entropy 22, br. 9 (2020). https://doi.org/10.3390/e22090994

Jerčić, M. i 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]. 07.09.2020. [pristupljeno 11.12.2024.];22(9). doi: 10.3390/e22090994

M. Jerčić i N. Poljak, "Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model", Entropy, vol. 22, br. 9, Rujan 2020. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:217:420392. [Citirano: 11.12.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:217:420392
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Podaci o radu
Naslov (engleski)Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
AutorMarko Jerčić
AutorNikola Poljak
Autorova ustanovaSveučilište u Zagrebu
Prirodoslovno-matematički fakultet
(Fizički odsjek)
Znanstveno / umjetničko
područje, polje i grana		PRIRODNE ZNANOSTI
Fizika
Sažetak (engleski)
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.
Ključne riječi (hrvatski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaEntropy
Brojčani podacivol. 22, br. 9, 994
p-ISSN1099-4300
DOIhttps://doi.org/10.3390/e22090994
URN:NBNurn:nbn:hr:217:420392
Datum objave publikacije2020-09-07
URL dokumentahttps://www.mdpi.com/1099-4300/22/9/994
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Javna napomena© 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.
Datum i vrijeme pohrane2020-11-20 15:07:00