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Quark-Gluon Jet Discrimination Using Convolutional Neural Networks
이상훈,박인규,WATSON IAN JAMES,양승진 한국물리학회 2019 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.74 No.3
Currently, newly developed articial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classication of particle physics collider data. One such challenging task is to distinguish quark-initiated jets from gluon-initiated jets. Following previous work, we treat the jet as an image by pixelizing track information and calorimeter deposits as reconstructed by the detector. We test the deep learning paradigm by training several recently developed, state-of-the-art convolutional neural networks on the quarkgluon discrimination task. We compare the results obtained using various network architectures trained for quark-gluon discrimination and also a boosted decision tree (BDT) trained on summary variables.
Creativity as the engine of mathematical discovery : Students as Partners in Learning
( Prabhu Vrunda ),( Barbatis Peter ),( Watson James ) 한국수학교육학회 2011 수학교육 학술지 Vol.2011 No.-
Creativity is a need in mathematics education. In this article we describe the beginnings of a creative approach aimed at eliminating the disenfranchisement students exhibit toward mathematics. Creative thinking is the sense making mechanism utilized in the created learning environment within the classroom and for student use through the instructional materials. The objective is to jumpstart students` natural curiosity, alleviate longheld fears about mathematics, and create an engaging thinking environment, to promote students to take ownership of their own learning, through the initiative Students as Partners in Learning.
Quark Gluon Jet Discrimination with Weakly Supervised Learning
이상훈,이상만,이윤재,박인규,WATSON IAN JAMES,양승진 한국물리학회 2019 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.75 No.9
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 classication, by using samples with different quark{ gluon proportions instead of fully labeled samples. This paradigm has, therefore, huge potential for particle physics classication 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 classication 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.
Measuring |Vts| directly using strange‑quark tagging at the LHC
Jang Woojin,Lee Jason SangHun,박인규,WATSON IAN JAMES 한국물리학회 2022 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.81 No.5
The Cabibbo–Kobayashi–Maskawa (CKM) element Vts, representing the coupling between the top and strange quarks, is currently best determined through fts based on the unitarity of the CKM matrix, and measured indirectly through box-diagram oscillations, and loop-mediated rare decays of the B or K mesons. It has been previously proposed to use the tree level decay of the t quark to the s quark to determine |Vts| at the LHC, which has become a top factory. In this paper, we extend the proposal by performing a detailed analysis of measuring t → sW in dileptonic t̄t events. In particular, we perform detector response simulation, including the reconstruction of K0 S , which are used for tagging jets produced by s quarks against the dominant t → bW decay. We show that it should be possible to exclude |Vts| = 0 at 5.5휎 with the expected High Luminosity LHC luminosity of 3000 fb−1 , considering only the statistical uncertainties, and not the systematic uncertainties which will play a role in setting the fnal analysis limits.