scholarly journals Measurements of jet fragmentation and jet substructure with ALICE

2019 ◽  
Author(s):  
Markus Fasel ◽  
Keyword(s):  
Jet Substructure ◽  
Jet Fragmentation

scholarly journals Overview of jet physics results from ALICE

2020 ◽  
Vol 235 ◽  
pp. 01002
Author(s):  
Filip Krizek
Keyword(s):  
Transverse Momentum ◽  
Heavy Ion ◽  
Jet Quenching ◽  
Jet Physics ◽  
Jet Substructure ◽  
Momentum Fraction ◽  
Complementary Information ◽  
Ion Collisions ◽  
Resolution Parameter ◽  
Jet Fragmentation

Recent results from jet shower-shape and substructure analyses done by the ALICE collaboration in central Pb–Pb collisions at √sNN = 2.76 TeV and in pp collisions at √s = 7 TeV are reviewed. The presented jet shower- shape observables are angularity g and transverse momentum dispersion pTD, which were studied for a small resolution parameter R = 0.2, track-based jets with a minimum constituent transverse momentum (pT) cut-off of 0.15 GeV/c. Jet substructure is explored for track-based anti-kT jets with R = 0.4 by means of iterative declustering and grooming techniques, which were used to measure the absolutely-normalized leading subjet momentum fraction zg and the number of hard splittings in the reclustered jet shower, nSD. These observables provide complementary information on the jet fragmentation and help to discriminate between different scenarios for medium-induced modifications of the parton shower in heavy-ion collisions due to jet quenching.

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

Quark Jet Fragmentation

1979 ◽  
Vol 19 (2) ◽  
pp. 184-190 ◽  
Author(s):  
B Andersson ◽  
G Gustafson ◽  
C Peterson
Keyword(s):  
Jet Fragmentation

Numerical Simulation of Corium Jet Breakup During Fuel-Coolant Interaction Based on the ALISA Test Performed at KROTOS Test Facility

2018 ◽  
Author(s):  
Pei Shen ◽  
Wenzhong Zhou
Keyword(s):  
Numerical Simulation ◽  
Steam Explosion ◽  
Field Method ◽  
Severe Accident ◽  
Phase Field Method ◽  
Test Facility ◽  
Jet Breakup ◽  
Jet Behavior ◽  
Jet Fragmentation ◽  
Explosion Test

Steam explosion is one of the consequences of fuel-coolant interactions in a severe accident. Melt jet fragmentation, which is the key phenomenon during steam explosion, has not been clarified sufficiently which prevents the precise prediction of steam explosion. The focus of this paper is on the numerical simulation of the melt jet behavior falling into a coolant pool in order to get a qualitative and quantitative understanding of initial premixing stage of fuel-coolant interaction. The objective of our first phase is the simulation of the fragmentation process and the estimation of the jet breakup length. A commercial CFD code COMSOL is used for the 2D numerical analysis employing the phase field method. The simulation condition is similar to our steam explosion test supported by the ALISA (Access to Large Infrastructure for Severe Accidents) project between European Union and China, and carried out in the KROTOS test facility at CEA, France. The simulation result is in relatively good agreement with the experimental data. Then the effect of the initial jet velocity, the jet diameter and the instability theory are presented. The preliminary data of melt jet fragmentation is helpful to understand the premixing stage of the fuel-coolant interaction.

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