# The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments Source: [The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments]() Collection index: [llms.txt]() Data updated: 2025-10-25T01:36:00.792Z Authors: Venkitesh Ayyar, Wahid Bhimji, Lisa Gerhardt, Sally Robertson, Zahra Ronaghi Published: 2020-02-13T19:54:46.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()