EPJ Web Conf.
Volume 245, 202024th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|Number of page(s)||7|
|Section||6 - Physics Analysis|
|Published online||16 November 2020|
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, California 94720, USA.
2 University of California, 101 Sproul Hall, Berkeley, California 94720, USA.
3 NVIDIA, 2788 San Tomas Expressway, Santa Clara, California 95051, USA.
* e-mail: email@example.com
Published online: 16 November 2020
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background.
In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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