Abstract
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.
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Kasieczka, G., Plehn, T., Russell, M. et al. Deep-learning top taggers or the end of QCD?. J. High Energ. Phys. 2017, 6 (2017). https://doi.org/10.1007/JHEP05(2017)006
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DOI: https://doi.org/10.1007/JHEP05(2017)006