Rare decays in flavor physics often suffer from Helicity suppress and Loop suppress.Helicity flip is a direct consequence of chiral Ue3T symmetry breaking and electroweak symmetry breaking.The identical feature is als...Rare decays in flavor physics often suffer from Helicity suppress and Loop suppress.Helicity flip is a direct consequence of chiral Ue3T symmetry breaking and electroweak symmetry breaking.The identical feature is also shared by the mass generation of SM fermions.In this review,we use the Minimal Supersymmetric Standard Model(MSSM)as an example to illustrate an explicit connection between bottom Yukawa coupling and rare decay process of b!sc.We take a symmetry approach to study the common symmetry breaking in supersymmetric correction to bottom quark mass generation and b!sc.We show that Large Peccei-Quinn symmetry breaking effect and R-symmetry breaking effect required by b!sc inevitably lead to significant reduction of bottom Yukawa yb.To compromise the reduction in b b,a new decay is also needed to keep the Higgs total width as the SM value.展开更多
Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images with the benefit tha...Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images with the benefit that essentially no physics input is required.Inspired by a recent work by Datta and Larkoski, we study the classification of quark/gluon-initiated jets based on fully-connected neural networks(FNNs), where expert-designed physical variables are taken as input. FNNs are applied in two ways: trained separately on various narrow jet transverse momentum pTJbins;trained on a wide region of pTJ∈[200, 1000] GeV. We find their performances are almost the same. The performance is better when the pTJis larger. Jet discrimination with FNN is studied on both particle and detector level data. The results based on particle level data are comparable with those from deep convolutional neural networks, while the significance improvement characteristic(SIC) from detector level data would at most decrease by 15%.We also test the performance of FNNs with the full set or subsets of jet observables as input features. The FNN with one subset consisting of fourteen observables shows nearly no degradation of performance. This indicates that these fourteen expert-designed observables could have captured the most necessary information for separating quark and gluon jets.展开更多
基金supported by the Zhejiang University Fundamental Research Funds for the Central Universities (2011QNA3017)the National Natural Science Foundation of China (11245002,11275168,11075139 and 11135006)Program for New Century Excellent Talents in University
文摘Rare decays in flavor physics often suffer from Helicity suppress and Loop suppress.Helicity flip is a direct consequence of chiral Ue3T symmetry breaking and electroweak symmetry breaking.The identical feature is also shared by the mass generation of SM fermions.In this review,we use the Minimal Supersymmetric Standard Model(MSSM)as an example to illustrate an explicit connection between bottom Yukawa coupling and rare decay process of b!sc.We take a symmetry approach to study the common symmetry breaking in supersymmetric correction to bottom quark mass generation and b!sc.We show that Large Peccei-Quinn symmetry breaking effect and R-symmetry breaking effect required by b!sc inevitably lead to significant reduction of bottom Yukawa yb.To compromise the reduction in b b,a new decay is also needed to keep the Higgs total width as the SM value.
基金supported by the German Science Foundation (DFG) within the Collaborative Research Center 676 “Particles, Strings and the Early Universe”the Recruitment Program of Global Youth Experts of China+1 种基金supported in part by the National Natural Science Foundation of China (Grant Nos. 11135006, 11275168, 11422544, 11375151, and 11535002)the Zhejiang University Fundamental Research Funds for the Central Universities (Grant No. 2017QNA3007)
文摘Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images with the benefit that essentially no physics input is required.Inspired by a recent work by Datta and Larkoski, we study the classification of quark/gluon-initiated jets based on fully-connected neural networks(FNNs), where expert-designed physical variables are taken as input. FNNs are applied in two ways: trained separately on various narrow jet transverse momentum pTJbins;trained on a wide region of pTJ∈[200, 1000] GeV. We find their performances are almost the same. The performance is better when the pTJis larger. Jet discrimination with FNN is studied on both particle and detector level data. The results based on particle level data are comparable with those from deep convolutional neural networks, while the significance improvement characteristic(SIC) from detector level data would at most decrease by 15%.We also test the performance of FNNs with the full set or subsets of jet observables as input features. The FNN with one subset consisting of fourteen observables shows nearly no degradation of performance. This indicates that these fourteen expert-designed observables could have captured the most necessary information for separating quark and gluon jets.
