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Title: Improving High-Energy Particle Detectors with Machine Learning

Abstract

Microseconds after the Big Bang, the universe existed in a state called the quark-gluon plasma (QGP). To experimentally study its properties, the QGP is recreated in high-energy nuclear collisions at the LHC, and the particles produced from the QGP are reconstructed from their energy deposition in the ATLAS calorimeter. This requires both classifying the particles and calibrating their deposited energy. The objective of this project is to improve the reconstruction by using machine learning techniques, where the energy depositions of clusters of cells, formed by ATLAS topo-clustering methods, are treated as three-dimensional images when inputted to neural networks. This approach significantly improves the calibration of deposited energies when cross-validating while training, and models trained on idealized data predict the calibrated energies of particles in more complex data sets well. Additionally, implementation of a data generator using uproot allows the program to load input data into memory as needed while training or predicting, significantly reducing the amount of memory used. The data generator also allows for use of multiprocessing to speed up training and evaluating. This work illustrates that using machine learning methods for both classification and calibration has the potential to significantly improve particle reconstruction.

Authors:
 [1];  [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1670544
Report Number(s):
LLNL-TR-814867
1023020
DOE Contract Number:  
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; 97 MATHEMATICS AND COMPUTING

Citation Formats

Heinz, Michael, Angerami, Aaron, Karande, Piyush, and Soltz, Ron. Improving High-Energy Particle Detectors with Machine Learning. United States: N. p., 2020. Web. doi:10.2172/1670544.
Heinz, Michael, Angerami, Aaron, Karande, Piyush, & Soltz, Ron. Improving High-Energy Particle Detectors with Machine Learning. United States. https://doi.org/10.2172/1670544
Heinz, Michael, Angerami, Aaron, Karande, Piyush, and Soltz, Ron. 2020. "Improving High-Energy Particle Detectors with Machine Learning". United States. https://doi.org/10.2172/1670544. https://www.osti.gov/servlets/purl/1670544.
@article{osti_1670544,
title = {Improving High-Energy Particle Detectors with Machine Learning},
author = {Heinz, Michael and Angerami, Aaron and Karande, Piyush and Soltz, Ron},
abstractNote = {Microseconds after the Big Bang, the universe existed in a state called the quark-gluon plasma (QGP). To experimentally study its properties, the QGP is recreated in high-energy nuclear collisions at the LHC, and the particles produced from the QGP are reconstructed from their energy deposition in the ATLAS calorimeter. This requires both classifying the particles and calibrating their deposited energy. The objective of this project is to improve the reconstruction by using machine learning techniques, where the energy depositions of clusters of cells, formed by ATLAS topo-clustering methods, are treated as three-dimensional images when inputted to neural networks. This approach significantly improves the calibration of deposited energies when cross-validating while training, and models trained on idealized data predict the calibrated energies of particles in more complex data sets well. Additionally, implementation of a data generator using uproot allows the program to load input data into memory as needed while training or predicting, significantly reducing the amount of memory used. The data generator also allows for use of multiprocessing to speed up training and evaluating. This work illustrates that using machine learning methods for both classification and calibration has the potential to significantly improve particle reconstruction.},
doi = {10.2172/1670544},
url = {https://www.osti.gov/biblio/1670544}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {9}
}