Quark Gluon Jet Discrimination with Weakly Supervised Learning
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Deep learning techniques are currently being investigated for high energy physics experiments, to tackle a wide range of problems, with quark and gluon discrimination becoming a benchmark for new algorithms. One weakness is the traditional reliance on Monte Carlo simulations, which may not be well modelled at the detail required by deep learning algorithms. The weakly supervised learning paradigm gives an alternate route to classification, by using samples with different quark-gluon proportions instead of fully labeled samples. This paradigm has, therefore, huge potential for particle physics classification problems as these weakly supervised learning methods can be applied directly to collision data. In this study, we show that realistically simulated samples of dijet and Z+jet events can be used to discriminate between quark and gluon jets by using weakly supervised learning. We implement and compare the performance of weakly supervised learning for quark-gluon jet classification using three different machine learning methods: the jet image-based convolutional neural network, the particle-based recurrent neural network and and the feature-based boosted decision tree.
KeywordsQCD Jet Fragmentation Weakly supervised learning Machine learning
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This article was supported by the computing resources of the Global Science Experimental Data Hub Center (GSDC) at the Korea Institute of Science and Technology Information (KISTI). J.L. and I.W. are supported by the Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (KRF project grant number: 2017H1D3A1A01052807). I.P., Y.L. and S.Y. are supported by the Basic Science Research Program through the NRF funded by the Ministry of Education (2018R1A6A1A06024977).
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