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- W4378469077 abstract "In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the design of future particle physics experiments and their data exploitation." @default.
- W4378469077 created "2023-05-27" @default.
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- W4378469077 date "2023-05-26" @default.
- W4378469077 modified "2023-10-06" @default.
- W4378469077 title "Artificial intelligence for improved fitting of trajectories of elementary particles in dense materials immersed in a magnetic field" @default.
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- W4378469077 doi "https://doi.org/10.1038/s42005-023-01239-4" @default.
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