scholarly journals Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

2021 ◽  
Vol 251 ◽  
pp. 03042
Author(s):  
Florian Rehm ◽  
Sofia Vallecorsa ◽  
Kerstin Borras ◽  
Dirk Krücker
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Network Architecture ◽  
High Energy Physics ◽  
Three Dimensional ◽  
High Energy ◽  
Generative Adversarial Networks ◽  
Convolutional Networks ◽  
Representational Power ◽  
Energy Physics

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo technique. We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude. We follow a previous approach which used three-dimensional convolutional neural networks and develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Additionally, we increased the number of parameters and the neural networks representational power, obtaining a higher accuracy. We compare our best convolutional 2D neural network architecture and evaluate it versus the previous 3D architecture and Geant4 data. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.

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 Fast simulation of the electromagnetic calorimeter response using Self-Attention Generative Adversarial Networks

2021 ◽  
Vol 251 ◽  
pp. 03043
Author(s):  
Fedor Ratnikov ◽  
Alexander Rogachev
Keyword(s):  
Network Architecture ◽  
High Energy Physics ◽  
Hadron Collider ◽  
High Energy ◽  
Electromagnetic Calorimeter ◽  
Generative Adversarial Networks ◽  
Fast Simulation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider need, so the experiment is in urgent need of new fast simulation techniques. The application of Generative Adversarial Networks is a promising solution to speed up the simulation while providing the necessary physics performance. In this paper we propose the Self-Attention Generative Adversarial Network as a possible improvement of the network architecture. The application is demonstrated on the performance of generating responses of the LHCb type of the electromagnetic calorimeter.

scholarly journals High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Florin Pop
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Numerical Methods ◽  
High Performance ◽  
Data Science ◽  
Experimental Validation ◽  
High Energy Physics ◽  
High Energy ◽  
Performance Computing ◽  
Energy Physics

Modern physics is based on both theoretical analysis and experimental validation. Complex scenarios like subatomic dimensions, high energy, and lower absolute temperature are frontiers for many theoretical models. Simulation with stable numerical methods represents an excellent instrument for high accuracy analysis, experimental validation, and visualization. High performance computing support offers possibility to make simulations at large scale, in parallel, but the volume of data generated by these experiments creates a new challenge for Big Data Science. This paper presents existing computational methods for high energy physics (HEP) analyzed from two perspectives: numerical methods and high performance computing. The computational methods presented are Monte Carlo methods and simulations of HEP processes, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation in HEP, and Random Matrix Theory used in analysis of particles spectrum. All of these methods produce data-intensive applications, which introduce new challenges and requirements for ICT systems architecture, programming paradigms, and storage capabilities.

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.

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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