Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 90 references that are in the map. First posted on arXiv 9 Nov 2022.
Builds on
20 of 90 references.
References · 20
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
- 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
- 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
- 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 415
- 2015LSTM: A Search Space Odysseycited by 315
- 2017Gate-Variants of Gated Recurrent Unit (GRU) Neural Networkscited by 239
8 more
- 2017Comparative Study of CNN and RNN for Natural Language Processingcited by 81
- 2018Machine Learning in High Energy Physics Community White Papercited by 48
- 2017Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesiscited by 46
- 2018Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networkscited by 29
- 2016A Convolutional Neural Network Neutrino Event Classifiercited by 26
- 2021Charged particle tracking via edge-classifying interaction networkscited by 19
- 2021Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectorscited by 11
- 2018Topology classification with deep learning to improve real-time event selection at the LHCcited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.