scholarly journals Semi-inclusive jet functions and jet substructure in JET(I) and JET(II) algorithms

2021 ◽  
Vol 103 (5) ◽  
Author(s):  
Lei Wang ◽  
Zhong-Bo Kang ◽  
Hongxi Xing ◽  
Ben-Wei Zhang
Keyword(s):  
Jet Substructure ◽  
Inclusive Jet

scholarly journals Jet substructure using semi-inclusive jet functions in SCET

2016 ◽  
Vol 2016 (11) ◽  
Author(s):  
Zhong-Bo Kang ◽  
Felix Ringer ◽  
Ivan Vitev
Keyword(s):  
Jet Substructure ◽  
Inclusive Jet

scholarly journals Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation

2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Garvita Agarwal ◽  
Lauren Hay ◽  
Ia Iashvili ◽  
Benjamin Mannix ◽  
Christine McLean ◽  
...  
Keyword(s):  
Network Performance ◽  
Deep Neural Network ◽  
Relevant Information ◽  
Jet Substructure ◽  
Discrimination Ability ◽  
Low Level ◽  
Performance Improvements ◽  
Intermediate Layers ◽  
Explainable Ai ◽  
General Method

Abstract A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs (“eXpert AUGmented” variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.

scholarly journals Search for $$ t\overline{t} $$ resonances in fully hadronic final states in pp collisions at $$ \sqrt{s} $$ = 13 TeV with the ATLAS detector

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
G. Aad ◽  
◽  
B. Abbott ◽  
D. C. Abbott ◽  
A. Abed Abud ◽  
...  
Keyword(s):  
Production Cross ◽  
Hadron Collider ◽  
Top Quarks ◽  
Atlas Detector ◽  
Proton Collision ◽  
Large Radius ◽  
Final States ◽  
Jet Substructure ◽  
High Transverse Momentum ◽  
Hadronic Final States

Abstract This paper presents a search for new heavy particles decaying into a pair of top quarks using 139 fb−1 of proton-proton collision data recorded at a centre-of-mass energy of $$ \sqrt{s} $$ s = 13 TeV with the ATLAS detector at the Large Hadron Collider. The search is performed using events consistent with pair production of high-transverse-momentum top quarks and their subsequent decays into the fully hadronic final states. The analysis is optimized for resonances decaying into a $$ t\overline{t} $$ t t ¯ pair with mass above 1.4 TeV, exploiting a dedicated multivariate technique with jet substructure to identify hadronically decaying top quarks using large-radius jets and evaluating the background expectation from data. No significant deviation from the background prediction is observed. Limits are set on the production cross-section times branching fraction for the new Z′ boson in a topcolor-assisted-technicolor model. The Z′ boson masses below 3.9 and 4.7 TeV are excluded at 95% confidence level for the decay widths of 1% and 3%, respectively.

scholarly journals Jet substructure classification in high-energy physics with deep neural networks

2016 ◽  
Vol 93 (9) ◽  
Author(s):  
Pierre Baldi ◽  
Kevin Bauer ◽  
Clara Eng ◽  
Peter Sadowski ◽  
Daniel Whiteson
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Jet Substructure ◽  
Energy Physics

scholarly journals Probing quarkonium production mechanisms with jet substructure

2014 ◽  
Vol 2014 (11) ◽  
Author(s):  
Matthew Baumgart ◽  
Adam K. Leibovich ◽  
Thomas Mehen ◽  
Ira Z. Rothstein
Keyword(s):  
Jet Substructure ◽  
Quarkonium Production ◽  
Production Mechanisms
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