ArticleEntropy (Basel, Switzerland)2020
Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Introduction and Analysis of a Method for the Investigation of QCD-like Tree Data.Entropy (Basel, Switzerland) · 2022Article
- Human-Centric AI: The Symbiosis of Human and Artificial Intelligence.Entropy (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
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.
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