Related papers: Automated Discovery of Jet Substructure Analyses
Jet substructure has emerged as a critical tool for LHC searches, but studies so far have relied heavily on shower Monte Carlo simulations, which formally approximate QCD at leading-log level. We demonstrate that systematic higher-order QCD…
The CMS experiment makes use of a large variety of algorithms to identify the origin of particle jets measured in the detector. Through the study of jet substructure properties, jets originating from quarks, gluons, W/Z/Higgs bosons, top…
The Large Hadron Collider (LHC) is the first machine that provides high enough energy to produce large numbers of boosted top quarks. The decay products of these top quarks are confined to a cone in the top quark flight direction and can be…
Recent experimental advances at the Relativistic Heavy Ion Collider (RHIC) and the large center-of-mass energies available to the heavy-ion program at the Large Hadron Collider (LHC) will enable strongly interacting matter at high…
We present a coherent model that combines jet production from perturbative QCD with strongly-coupled jet-medium interactions described in holography. We use this model to study the modification of an ensemble of jets upon propagation…
Jets are collimated sprays of particles resulting from fragmentation of hard scattered partons. They are measured in different types of collisions at different energies to test perturbative Quantum Chromodynamic calculations and are used to…
To probe the properties of the quark-gluon plasma created in heavy-ion collisions, a very useful class of observables refers to the propagation of energetic jets. A jet is a collimated spray of particles generated via successive parton…
The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed to classify jets according to their…
Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of particle physics collider data. One such challenging task is…
Exploiting the substructure of jets observed at the LHC to better understand and interpret the experimental data has recently been a very active area of research. In this thesis we study the substructure of high-p$_T$ QCD jets, which form a…
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential…
The process by which jet algorithms construct jets and subjets is inherently ambiguous and equally well motivated algorithms often return very different answers. The Qjets procedure was introduced by the authors to account for this…
This work aims at the performance of the ALICE detector for the measurement of high-energy jets at mid-pseudo-rapidity in ultra-relativistic nucleus--nucleus collisions at LHC and their potential for the characterization of the partonic…
We investigate the substructure of jets produced in photoproduction events with center-of-mass energies $\sqrt{s} = 30\text{--}140$ GeV at the proposed Electron-Ion Collider (EIC). Events are generated using PYTHIA and contributions from…
The relationship between jet properties and the underlying geometry of the medium produced in heavy ion collisions can be explored through a measurement of the correlation between the axes of reconstructed jets and the reaction plane,…
Jet substructure has emerged to play a central role at the Large Hadron Collider, where it has provided numerous innovative ways to search for new physics and to probe the Standard Model, particularly in extreme regions of phase space. In…
Predictions are made for the jet substructure of one-jet events produced in electron-proton neutral current deep inelastic scattering at the future Electron-Ion Collider for exchanged four-momentum squared, $Q^2 > 125$ GeV$^2$. Data are…
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…
We present a topology-informed approach for classifying particle jets using persistent homology, a framework that captures the structural properties of point clouds. Particle jets produced in proton-proton collisions consist of cascades of…
Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36-81 fb$^{-1}$ of proton-proton collision data with a centre-of-mass energy of $\sqrt{s}=13$ TeV collected by the ATLAS detector…