scholarly journals Application of a Convolutional Neural Network for image classification for the analysis of collisions in High Energy Physics

2019 ◽  
Vol 214 ◽  
pp. 06017 ◽  
Author(s):  
Celia Fernández Madrazo ◽  
Ignacio Heredia ◽  
Lara Lloret ◽  
Jesús Marco de Lucas
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Energy Physics ◽  
Open Data ◽  
Feedforward Neural Networks ◽  
Relevant Information ◽  
High Energy ◽  
Particle Collisions ◽  
Learning Framework ◽  
Energy Physics

The application of deep learning techniques using convolutional neural networks for the classification of particle collisions in High Energy Physics is explored. An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed. The idea is tested using a well-known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data. This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks.

scholarly journals Jet substructure classification in high-energy physics with deep neural networks

2016 ◽  
Vol 93 (9) ◽  
Author(s):  
Pierre Baldi ◽  
Kevin Bauer ◽  
Clara Eng ◽  
Peter Sadowski ◽  
Daniel Whiteson
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Jet Substructure ◽  
Energy Physics

scholarly journals Deep Learning and Its Application to LHC Physics

2018 ◽  
Vol 68 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Dan Guest ◽  
Kyle Cranmer ◽  
Daniel Whiteson
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Learning Tools ◽  
Future Prospects ◽  
Higher Dimensional ◽  
Lhc Physics ◽  
Core Concepts ◽  
Energy Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

scholarly journals HIPSTER

2019 ◽  
Vol 214 ◽  
pp. 06027
Author(s):  
Adrian Bevan ◽  
Thomas Charman ◽  
Jonathan Hays
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Event Recognition ◽  
The Core ◽  
Rich Information ◽  
The Rich ◽  
Core Functionality ◽  
Python Package ◽  
Energy Physics

HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in detail.

Black holes and high energy physics

2016 ◽  
Vol 31 (02n03) ◽  
pp. 1641016
Author(s):  
A. A. Grib ◽  
Yu. V. Pavlov
Keyword(s):  
Black Holes ◽  
High Energy Physics ◽  
High Energy ◽  
Ultra High Energy ◽  
Particle Collisions ◽  
High Energy Cosmic Rays ◽  
High Energies ◽  
The Earth ◽  
Superheavy Dark Matter ◽  
Energy Physics

Three mechanisms of getting high energies in particle collisions in the ergosphere of the rotating black holes are considered. The consequences of these mechanisms for observation of ultra high energy cosmic rays particles on the Earth as result of conversion of superheavy dark matter particles into ordinary particles are discussed.

scholarly journals The Use of Neural Networks in High-Energy Physics

1993 ◽  
Vol 5 (4) ◽  
pp. 505-549 ◽  
Author(s):  
Bruce Denby
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
High Energy Physics ◽  
High Energy ◽  
High Quality ◽  
Network Algorithms ◽  
The Past ◽  
The Neural Network ◽  
Energy Physics

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

scholarly journals Adaptive Calibration of Imaging Array Detectors

1999 ◽  
Vol 11 (6) ◽  
pp. 1281-1296
Author(s):  
Marco Budinich ◽  
Renato Frison
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
Infrared Imaging ◽  
High Energy ◽  
Silicon Detectors ◽  
Adaptive Calibration ◽  
Imaging Array ◽  
Array Detectors ◽  
Linear Correction ◽  
Energy Physics

We present two methods for nonuniformity correction of imaging array detectors based on neural networks; both exploit image properties to supply lack of calibrations and maximize the entropy of the output. The first method uses a self-organizing net that produces a linear correction of the raw data with coefficients that adapt continuously. The second method employs a kind of contrast equalization curve to match pixel distributions. Our work originates from silicon detectors, but the treatment is general enough to be applicable to many kinds of array detectors like those used in infrared imaging or in high-energy physics.

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