Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model

Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model

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...

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Main Authors: Marko Jercic, Nikola Poljak
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Entropy
Subjects:
quantum chromodynamics
network model
data analysis
interpretability
Online Access:https://www.mdpi.com/1099-4300/22/9/994
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spelling doaj-8ad365158d724e33b3e792d21e64305d2020-11-25T03:18:59ZengMDPI AGEntropy1099-43002020-09-012299499410.3390/e22090994Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network ModelMarko Jercic0Nikola Poljak1Department of Physics, Faculty of Science, University of Zagreb, 10 000 Zagreb, CroatiaDepartment of Physics, Faculty of Science, University of Zagreb, 10 000 Zagreb, CroatiaThe 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.https://www.mdpi.com/1099-4300/22/9/994quantum chromodynamicsnetwork modeldata analysisinterpretability
collection DOAJ
language English
format Article
sources DOAJ
author Marko Jercic
Nikola Poljak
spellingShingle Marko Jercic
Nikola Poljak
Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
Entropy
quantum chromodynamics
network model
data analysis
interpretability
author_facet Marko Jercic
Nikola Poljak
author_sort Marko Jercic
title Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
title_short Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
title_full Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
title_fullStr Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
title_full_unstemmed Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model
title_sort exploring the possibility of a recovery of physics process properties from a neural network model
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2020-09-01
description 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.
topic quantum chromodynamics
network model
data analysis
interpretability
url https://www.mdpi.com/1099-4300/22/9/994
work_keys_str_mv AT markojercic exploringthepossibilityofarecoveryofphysicsprocesspropertiesfromaneuralnetworkmodel
AT nikolapoljak exploringthepossibilityofarecoveryofphysicsprocesspropertiesfromaneuralnetworkmodel
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