تقرير
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments
| العنوان: | The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments |
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| المؤلفون: | Ayyar, Venkitesh, Bhimji, Wahid, Gerhardt, Lisa, Robertson, Sally, Ronaghi, Zahra |
| المصدر: | EPJ Web of Conferences 245, 06003 (2020) |
| سنة النشر: | 2020 |
| المجموعة: | High Energy Physics - Experiment Statistics |
| مصطلحات موضوعية: | High Energy Physics - Experiment, Statistics - Machine Learning |
| الوصف: | 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. Comment: Contribution to Proceedings of CHEP 2019, Nov 4-8, Adelaide, Australia |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1051/epjconf/202024506003 |
| URL الوصول: | http://arxiv.org/abs/2002.05761 |
| رقم الأكسشن: | edsarx.2002.05761 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1051/epjconf/202024506003 |
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