Abstract
Machine learning is increasingly being used not only in engineering applications such as computer vision and speech recognition, but in data analysis for the natural sciences. Here we describe applications of deep learning to four areas of experimental sub-atomic physics — high-energy physics, antimatter physics, neutrino physics, and dark matter physics.
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Sadowski, P., Baldi, P. (2018). Deep Learning in the Natural Sciences: Applications to Physics. In: Rozonoer, L., Mirkin, B., Muchnik, I. (eds) Braverman Readings in Machine Learning. Key Ideas from Inception to Current State. Lecture Notes in Computer Science(), vol 11100. Springer, Cham. https://doi.org/10.1007/978-3-319-99492-5_12
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