Related papers: B-jet Tagging Using a Hybrid Edge Convolution and …
Deep learning techniques have shown the capability to identify the degree of energy loss of high-energy jets traversing hot QCD medium on a jet-by-jet basis. The average amount of quenching of quark and gluon jets in hot QCD medium actually…
Measurements are presented of the jet invariant mass and substructure in proton-proton collisions at sqrt{s} = 7 TeV with the ATLAS detector using an integrated luminosity of 37 pb-1. These results exercise the tools for distinguishing the…
Jet tagging is a classification problem in high-energy physics experiments that aims to identify the collimated sprays of subatomic particles, jets, from particle collisions and tag them to their emitter particle. Advances in jet tagging…
Top tagging is a recent approach to identifying boosted hadronic top quarks. It avoids reconstructing individual top decay products and instead uses a jet algorithm to reconstruct the entire top decay. Quite generally, geometrically large…
The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration,…
The identification of jets and their constituents is one of the key problems and challenging task in heavy ion experiments such as experiments at RHIC and LHC. The presence of huge background of soft particles pose a curse for jet finding…
Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc. Among those, jet classification using neural…
Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement.…
Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between $b$-quark,…
The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method…
We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of $\sqrt{s} =$ 13 TeV at the CERN LHC. The algorithm is trained on a large…
In this work, we introduce a new jet observable, the one-point energy correlators (EC), designed to characterize the in-jet energy flow distribution by measuring energy deposition at a specific angle relative to the jet axis. Building upon…
Jet quenching, the modification of jets by the quark-gluon plasma in heavy-ion collisions, provides a sensitive probe of the properties of the medium. A jet-by-jet discrimination study between proton-proton and lead-lead jets using energy…
Many physics signals presently studied at the high energy collision experiments lead to final states with jets originating from heavy flavor quarks. This report reviews the algorithms for heavy flavor jets identification developed by the…
The identification of jets containing a $b$-hadron, referred to as $b$-tagging, plays an important role for various physics measurements and searches carried out by the ATLAS experiment at the CERN Large Hadron Collider (LHC). The most…
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…
We compare the performance of a convolutional neural network (CNN) trained on jet images with dense neural networks (DNNs) trained on n-subjettiness variables to study the distinguishing power of these two separate techniques applied to top…
The study of the substructure of collimated particles from quarks and gluons, or jets, has the promise to reveal the details how color charges interact with the QCD plasma medium created in colliders such as RHIC and the LHC. Traditional…
We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) -- to build a highly effective boosted top tagger. Previous work (the "DeepTop" tagger of Kasieczka et al) has shown that a CNN-based top…
We present a comprehensive study of jet substructure modifications in high-energy heavy-ion collisions using both inclusive jets and $\gamma$-tagged jets, based on a multi-stage jet evolution model within the Monte Carlo framework JETSCAPE.…