Hadrons, better, faster, stronger
نویسندگان
چکیده
Abstract Motivated by the computational limitations of simulating interactions particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on two important fronts. First, previously investigated Wasserstein generative adversarial network bounded information bottleneck autoencoder models are improved successful learning hadronic showers initiated charged pions segment calorimeter International Large Detector is demonstrated for first time. Second, we consider how state-of-the-art reconstruction software applied generated energies affects obtainable energy response resolution. While many challenges remain, these results constitute an milestone using realistic setting.
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ژورنال
عنوان ژورنال: Machine learning: science and technology
سال: 2022
ISSN: ['2632-2153']
DOI: https://doi.org/10.1088/2632-2153/ac7848