scholarly journals Quantum machine learning and its supremacy in high energy physics

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Particle Physics ◽  
High Energy Physics ◽  
Recognition Task ◽  
High Energy ◽  
Particle Identification ◽  
Quantum Machine Learning ◽  
The Future ◽  
Energy Physics

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.

scholarly journals Calorimetry with deep learning: particle simulation and reconstruction for collider physics

2020 ◽  
Vol 80 (7) ◽  
Author(s):  
Dawit Belayneh ◽  
Federico Carminati ◽  
Amir Farbin ◽  
Benjamin Hooberman ◽  
Gulrukh Khattak ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Particle Identification ◽  
Training Data ◽  
Particle Simulation ◽  
Collider Physics ◽  
Cell Level ◽  
Reconstruction Model ◽  
Energy Physics

Abstract Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

scholarly journals Particle identification for Higgs physics at future electron–positron collider

2020 ◽  
Vol 35 (15n16) ◽  
pp. 2041013 ◽  
Author(s):  
Wei-Ming Yao
Keyword(s):  
Higgs Physics ◽  
Yukawa Coupling ◽  
High Energy Physics ◽  
Flavor Physics ◽  
High Energy ◽  
Particle Identification ◽  
Heavy Flavor ◽  
Electron Positron ◽  
Physics Experiments ◽  
Energy Physics

Particle identification (PID) plays a key role in heavy-flavor physics in high-energy physics experiments. However, its impact on Higgs physics is still not clear. In this note, we will explore some of the potential of PID to improve the identification of heavy-flavor jets by using identified charged kaons in addition to the traditional vertexing information. This could result in a better measurement of the Higgs-charm Yukawa coupling at the future [Formula: see text] colliders.

scholarly journals Multigap Resistive Plate Chambers for Time of Flight Applications

2020 ◽  
Vol 11 (1) ◽  
pp. 111
Author(s):  
Yi Wang ◽  
Yancheng Yu
Keyword(s):  
High Performance ◽  
High Energy Physics ◽  
Low Cost ◽  
Time Of Flight ◽  
High Energy ◽  
Particle Identification ◽  
Flight System ◽  
Multigap Resistive Plate Chamber ◽  
Physics Experiments ◽  
Energy Physics

With the advantages of high-performance, easy to build and relatively low cost, the multigap resistive plate chamber has been arousing broad interests over the last few decades. It has become a new standard technology for the time of flight system in high energy physics experiments. In this article, we will give a description of the structure and the operating principles of the MRPC detector and focus on reviewing the applications on the time of flight system in several famous experiments. The performances, including time resolution and particle identification, are discussed in detail. Some recent advances and points of view for the future development of the next generation MRPC are also outlined.

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