Speaker
Description
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.
In this poster, I will discuss our efforts to apply CNNs to 3D image data from particle physics experiments to classify signal and background.
In this work, we present an extensive 3D 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 experiment detector and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with 3D-CNNs with less parameters, and present comparisons of computational requirements, training and inference times.
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