HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch

Abstract

HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It aims to ease the access to convolutional neural networks for applications that rely on hexagonally sampled data as, for example, commonly found in ground-based astroparticle physics experiments.

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• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• A Deep Learning-based Reconstruction of Cosmic Ray-induced Air Showers
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Cited by in corpus (8)

• Deep Ensemble Analysis for Imaging X-ray Polarimetry
• Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework
• The H.E.S.S. transients follow-up system
• Deep learning techniques for Imaging Air Cherenkov Telescopes
• Joint machine learning and analytic track reconstruction for X-ray polarimetry with gas pixel detectors
• Mitigating Polarization Leakage in Gas Pixel Detectors through Hybrid Machine Learning and Analytic Event Reconstruction
• Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation
• Neural network analysis of X-ray polarimeter data