Jet Reconstruction Research Articles

Optimisation of large-radius jet reconstruction for the ATLAS detector in 13\xa0TeV proton\u2013proton collisions

Jet substructure has provided new opportunities for searches and measurements at the LHC, and has seen continuous development since the optimization of the large-radius jet definition used by ATLAS was performed during Run 1. A range of new inputs to jet reconstruction, pile-up mitigation techniques and jet grooming algorithms motivate an optimisation of large-radius jet reconstruction for ATLAS. In this paper, this optimisation procedure is presented, and the performance of a wide range of large-radius jet definitions is compared. The relative performance of these jet definitions is assessed using metrics such as their pileup stability, ability to identify hadronically decaying W bosons and top quarks with large transverse momenta. A new type of jet input object, called a ‘unified flow object’ is introduced which combines calorimeter- and inner-detector-based signals in order to achieve optimal performance across a wide kinematic range. Large-radius jet definitions are identified which significantly improve on the current ATLAS baseline definition, and their modelling is studied using pp collisions recorded by the ATLAS detector at sqrt{s}=13~text {TeV} during 2017.

Reconstructing boosted Higgs jets from event image segmentation

Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to the traditional jet substructure analysis. In this work, the Mask R-CNN framework is adopted to reconstruct Higgs jets in collider-like events, with the effects of pileup contamination taken into account. This automatic jet reconstruction method achieves higher efficiency of Higgs jet detection and higher accuracy of Higgs boson four-momentum reconstruction than traditional jet clustering and jet substructure tagging methods. Moreover, the Mask R-CNN trained on events containing a single Higgs jet is capable of detecting one or more Higgs jets in events of several different processes, without apparent degradation in reconstruction efficiency and accuracy. The outputs of the network also serve as new handles for the toverline{t} background suppression, complementing to traditional jet substructure variables.

Jas4pp — A data-analysis framework for physics and detector studies

This paper describes the Jas4pp framework for exploring physics cases and for detector-performance studies of future particle collision experiments. Jas4pp is a multi-platform Java program for numeric calculations, scientific visualization in 2D and 3D, storing data in various file formats and displaying collision events and detector geometries. It also includes complex data-analysis algorithms for function minimization, regression analysis, event reconstruction (such as jet reconstruction), limit settings and other libraries widely used in particle physics. The framework can be used with several scripting languages, such as Python/Jython, Groovy and JShell. Several benchmark tests discussed in the paper illustrate significant improvements in the performance of the Groovy and JShell scripting languages compared to the standard Python implementation in C. The improvements for numeric computations in Java are attributed to recent enhancements in the Java Virtual Machine. Program summaryProgram title: Jas4ppCPC Library link to program files:https://doi.org/10.17632/jzvddk26cy.1Developer’s repository link:https://atlaswww.hep.anl.gov/asc/jas4pp/Licensing provisions: GNU General Public License 3Programming language: Java, Jython, GroovyNature of problem: Develop a platform-independent data-analysis framework for high-energy and nuclear physics (HEP and NP) with a support of fast dynamically-typed scripting languages, comprehensive data-visualisation and I/O libraries.Solution method: The solution adopted here is to use Java and the scripting languages integrated with Java VM.Additional comments: All 3rd party Java libraries included with this program are licensed by GPLv3, GNU Lesser General Public License (LGPL) or by other licenses compatible with the GPLv3 license, and adhere to Mendeley Data approved open-source software licenses. These licenses files are includes with the program.

Inclusive prompt photon-jet correlations as a probe of gluon saturation in electron-nucleus scattering at small x

We compute the differential cross-section for inclusive prompt photon+quark production in deeply inelastic scattering of electrons off nuclei at small x (e + A DIS) in the framework of the Color Glass Condensate effective field theory. The result is expressed as a convolution of the leading order (in the strong coupling αs) impact factor for the process and universal dipole matrix elements, in the limit of hard photon transverse momentum relative to the nuclear saturation scale Qs,A(x). We perform a numerical study of this process for the kinematics of the Electron-Ion Collider (EIC), exploring in particular the azimuthal angle correlations between the final state photon and quark. We observe a systematic suppression and broadening pattern of the back-to-back peak in the relative azimuthal angle distribution, as the saturation scale is increased by replacing proton targets with gold nuclei. Our results suggest that photon+jet final states in inclusive e + A DIS at high energies are in general a promising channel for exploring gluon saturation that is complementary to inclusive and diffractive dijet production. They also provide a sensitive empirical test of the universality of dipole matrix elements when compared to identical measurements in proton-nucleus collisions. However because photon+jet correlations at small x in EIC kinematics require jet reconstruction at small k⊥, it will be important to study their feasibility relative to photon-hadron correlations.

Reframing Jet Physics with New Computational Methods

We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element – parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space. We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques from statistics, machine learning, and combinatorial optimization. We also review some of the recent research in this direction that has been enabled with Ginkgo. We show how probabilistic programming can be used to efficiently sample the showering process, how a novel trellis algorithm can be used to efficiently marginalize over the enormous number of clustering histories for the same observed particles, and how the dynamic programming and reinforcement learning can be used to find the maximum likelihood clusterinng in this enormous search space. This work builds bridges with work in hierarchical clustering, statistics, combinatorial optmization, and reinforcement learning.

Jet Single Shot Detection

We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of calorimeter cells and using a Single Shot Detection network, called Jet-SSD. The model performs simultaneous localization and classification and additional regression tasks to measure jet features. We investigate TernaryWeight Networks with weights constrained to {-1, 0, 1} times a layer- and channel-dependent scaling factors. We show that the quantized version of the network closely matches the performance of its full-precision equivalent.

Heavy flavor jets in ALICE

Heavy-flavor quarks are considered to be effective probes of the Quark–Gluon Plasma (QGP) produced in ultra-relativistic heavy-ion collisions. Since quarks have a large mass, their production takes place mostly in initial hard processes, and it is calculable using perturbative QCD. Thus, heavy flavor quarks can be considered as ideal early-generated penetrating probes of the created medium and utilized to investigate mass-dependent properties of in-medium parton energy loss. Moreover, the measurement of heavy-flavor jet production in pp, besides being a natural reference for Pb–Pb studies, allows testing pQCD calculations and models of charm fragmentation in vacuum. In addition, similar measurements in p–Pb collisions allow assessing the importance of cold nuclear matter effects. The ALICE experiment at the LHC has excellent particle tracking capabilities, that allow for a precise jet reconstruction and for the identification of [Formula: see text]-meson and beauty hadron decay vertices, displaced hundreds of micrometers from the primary interaction vertex. In this proceedings, we will report the latest heavy-flavor jet measurements performed in p–Pb and pp collisions.

ECAL trigger performance in Run 2 and improvements for Run 3

The CMS electromagnetic Calorimeter (ECAL) is a high resolution crystal calorimeter operating at the CERN LHC. It is responsible for the identification and precise reconstruction of electrons and photons in CMS, which were crucial in the discovery and subsequent characterization of the Higgs boson. It also contributes to the reconstruction of tau leptons, jets, and calorimeter energy sums, which are vital components of many CMS physics analyses. The ECAL trigger system employs fast digital signal processing algorithms to precisely measure the energy and timing information of ECAL energy deposits recorded during LHC collisions. These trigger primitives are transmitted to the Level-1 trigger system at the LHC collision rate of 40 MHz. These energy deposits are then combined with information from other CMS sub-detectors to determine whether the event should trigger the readout of the data from CMS to be transmitted to the High-Level Trigger and eventually to permanent storage. This note will summarize the ECAL trigger performance achieved during LHC Run 2 (2015–2018). It will describe the methods that are used to provide frequent calibrations of the ECAL trigger primitives during LHC operation. These are needed to account for radiation-induced changes in crystal and photodetector response and to maintain stable trigger rates and efficiencies up to 0|η|=3.. They also minimize the spurious triggering on direct signals in the photodetectors used in the barrel region (|η|<1.48). Both of these effects have increased relative to LHC Run 1 (2009–2012), due to the higher luminosities experienced in Run 2. Further improvements in the energy and time reconstruction of the CMS ECAL trigger primitives are being explored for LHC Run 3 (2021–2023), using additional features implemented in the on-detector readout. These are particularly focused on improving the performance at the highest instantaneous luminosities (which will reach or exceed 2⋅ 1034 cm−2 s−1 in Run 3) and in the most forward regions of the calorimeter (|η|>2.5), where the effects of detector aging will be the greatest. The main features of these improved algorithms will be described and preliminary estimates of the expected performance gains will be presented.

Measurement of the Jet Mass Distribution and Top Quark Mass in Hadronic Decays of Boosted Top Quarks in pp Collisions at sqrt[s]=13 TeV.

A measurement is reported of the jet mass distribution in hadronic decays of boosted top quarks produced in pp collisions at sqrt[s]=13 TeV. The data were collected with the CMS detector at the LHC and correspond to an integrated luminosity of 35.9 fb^{-1}. The measurement is performed in the lepton+jets channel of tt[over ¯] events, where the lepton is an electron or muon. The products of the hadronic top quark decay t→bW→bqq[over ¯]^{'} are reconstructed as a single jet with transverse momentum larger than 400GeV. The tt[over ¯] cross section as a function of the jet mass is unfolded at the particle level and used to extract a value of the top quark mass of 172.6±2.5 GeV. A novel jet reconstruction technique is used for the first time at the LHC, which improves the precision by a factor of 3 relative to an earlier measurement. This highlights the potential of measurements using boosted top quarks, where the new technique will enable future precision measurements.

Detector and Physics Performance at a Muon Collider

A muon collider represents the ideal machine to reach very high center-of-mass energies and luminosities by colliding elementary particles. This is the result of the low level of beamstrahlung and synchrotron radiation compared to linear or circular electron-positron colliders. In contrast with other lepton machines, the design of a detector for a multi-TeV muon collider requires detailed knowledge of the interaction region due to the significant backgrounds created by muon beam decays in the collider ring. The physics reach can be properly evaluated only when the detector performance in such an environment is determined. In this work, the backgrounds generated by muon beams of 750 GeV are characterized and the performance of the tracking system and the calorimeter detector is illustrated. Solutions to minimize the effect of the beam-induced backgrounds are discussed and applied to obtain track and jet reconstruction performance. The μ+μ−→ Hνν̄→ bb̄ νν̄ process is fully simulated and reconstructed to demonstrate that physics measurements are possible in this harsh environment. The measurement precision for the Higgs boson coupling to bb̄ is evaluated for √s=1.5, 3, and 10 TeV and compared to other proposed machines.

Novel tools and observables for jet physics in heavy-ion collisions

Studies of fully-reconstructed jets in heavy-ion collisions aim at extracting thermodynamical and transport properties of hot and dense QCD matter. Recently, a plethora of new jet substructure observables have been theoretically and experimentally developed that provide novel precise insights on the modifications of the parton radiation pattern induced by a QCD medium. This report, summarizing the main lines of discussion at the 5th Heavy Ion Jet Workshop and CERN TH institute ‘Novel tools and observables for jet physics in heavy-ion collisions’ in 2017, presents a first attempt at outlining a strategy for isolating and identifying the relevant physical processes that are responsible for the observed medium-induced jet modifications. These studies combine theory insights, based on the Lund parton splitting map, with sophisticated jet reconstruction techniques, including grooming and background subtraction algorithms.

Reconstruction of neutral-triggered charged recoil jets in √s = 200 GeV p+p collisions at the STAR experiment

Jets — collimated sprays of hadrons — are produced by hard-scattered partons during the early stages of heavy ion collisions. Hence, they provide a valuable probe of the complex multi-particle dynamics within the hot, dense medium produced in such collisions. In particular, the study of jets recoiling from direct photons (γdir+jet) and those recoiling from energetic π0 (π0+jet) may shed light on the path-length and initial flavor (quark vs. gluon) dependence of the energy-loss experienced by a parton as it traverses the medium. We present here measurements of the yields of charged recoil jets tagged by γdir and π0 in p+p collisions at √S = 200 GeV. These measurements will serve as a vacuum fragmentation reference for an upcoming measurement in Au + Au collisions.

Exotic searches with the ATLAS detector

The summary is given for the exotic, non-SUSY searches for new physics with the ATLAS detector at Large Hadron Collider(LHC). The results presented use data collected at center-of-mass energies of , for data sets corresponding to a variety of integrated luminosities. Searches using leptons, photons, missing transverse energy, and jets are performed, as well as searches requiring custom jet and track reconstruction, and searches for so-called lepton jets. No deviations from Standard Model expectations are observed, hence constraints are placed on the phase space of available theoretical models.

Jet Structure Studies in Small Systems

A study investigating a possible jet shape dependence on the charged event multiplicity was performed on collision samples generated by Monte–Carlo (MC) event generators Pythia and Hijing++. We calculated the integral jet shape and found a significant modification caused by multiple-parton interactions. By interchanging and enabling different model ingredients in the simulations and analyzing the results in several &#x0D; &#x0D; &#x0D; &#x0D; p&#x0D; T&#x0D; &#x0D; &#x0D; &#x0D; bins and event multiplicity classes, we found a characteristic jet size measure that was independent of the chosen tunes, settings, and jet reconstruction algorithms.

Upgrade of the ATLAS detectors and trigger for the High Luminosity LHC: Tracking and timing for pile-up suppression

The High Luminosity-Large Hadron Collider is expected to start data-taking in 2026 and to provide an integrated luminosity of 3000 fb −1 , giving a factor 10 more data than that will be collected by 2023. This high statistics will make it possible to perform precise measurements in the Higgs sector and improve searches of new physics at the TeV scale. The peak luminosity is expected to be 7.5 × 10 34 cm −2 s −1 , corresponding to about 200 proton–proton interactions per event (pile-up), which will increase the rates at each level of the trigger and degrade the reconstruction performance. To cope with such a harsh environment many sub-detectors of the ATLAS experiment will be upgraded and some completely substituted. The Trigger-DAQ system will be upgraded. In this talk an overview of two new sub-detectors enabling powerful pile-up suppression, a new Inner Tracker and a High Granularity Timing Detector, will be given, describing the two technologies, their performance, and their interplay. Emphasis will also be given to the possibility of using tracking and timing information at the earliest, hardware based, ATLAS trigger stage. • Pileup suppression techniques for jets reconstruction at HL-LHC. • Tracking and Timing detectors of ATLAS detector at HL-LHC. • Tracking for Trigger at HL-LHC.

Deep learning in jet reconstruction at CMS

Deep learning has led to several breakthroughs outside the field of high energy physics, yet in jet reconstruction for the CMS experiment at the CERN LHC it has not been used so far. This report shows results of applying deep learning strategies to jet reconstruction at the stage of identifying the original parton association of the jet (jet tagging), which is crucial for physics analyses at the LHC experiments. We introduce a custom deep neural network architecture for jet tagging. We compare the performance of this novel method with the other established approaches at CMS and show that the proposed strategy provides a significant improvement. The strategy provides the first multi-class classifier, instead of the few binary classifiers that previously were used, and thus yields more information and in a more convenient way. The performance results obtained with simulation imply a significant improvement for a large number of important physics analysis at the CMS experiment.

Jet measurements in heavy ion physics

A hot, dense medium called a Quark Gluon Plasma (QGP) is created in ultrarelativistic heavy ion collisions. Hard parton scatterings generate high momentum partons that traverse the medium, which then fragment into sprays of particle called jets. Experimental measurements from high momentum hadrons, two particle correlations, and full jet reconstruction at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) continue to improve our understanding of energy loss in the QGP. Run 2 at the LHC recently began and there is a jet detector at RHIC under development. Now is the perfect time to reflect on what the experimental measurements have taught us so far, the limitations of the techniques used for studying jets, how the techniques can be improved, and how to move forward with the wealth of experimental data such that a complete description of energy loss in the QGP can be achieved. Measurements of jets to date clearly indicate that hard partons lose energy. Detailed comparisons of the nuclear modification factor between data and model calculations led to quantitative constraints on the opacity of the medium to hard probes. While there is substantial evidence for softening and broadening jets through medium interactions, the difficulties comparing measurements to theoretical calculations limit further quantitative constraints on energy loss mechanisms. We call for an agreement between theorists and experimentalists on the appropriate treatment of the background, Monte Carlo generators that enable experimental algorithms to be applied to theoretical calculations, and a clear understanding of which observables are most sensitive to the properties of the medium, even in the presence of background. This will enable us to determine the best strategy for the field to improve quantitative constraints on properties of the medium in the face of these challenges.

The Operating Principles of an Algorithm for Jet Reconstruction Using a Calorimeter and Trackers in Collisions of High-Energy Particles

On the basis of data on proton–proton collisions at the energy $$\sqrt s $$ = 8 TeV and with the integrated luminosity of 19.8 fb–1, the resolution of the transverse momentum and the uncertainty of the energy scale were estimated for jets that were reconstructed using the Jet-Plus-Track algorithm. The performance of the algorithm for the identification of jets that originated from the signal event and the separation of jets into quark and gluon jets was also assessed.

Jet reconstruction at high-energy electron\u2013positron colliders

In this paper we study the performance in e^+e^- collisions of classical e^+e^- jet reconstruction algorithms, longitudinally invariant algorithms and the recently proposed Valencia algorithm. The study includes a comparison of perturbative and non-perturbative jet energy corrections and the response under realistic background conditions. Several algorithms are benchmarked with a detailed detector simulation at sqrt{s}= 3 TeV. We find that the classical e^+e^- algorithms, with or without beam jets, have the best response, but they are inadequate in environments with non-negligible background. The Valencia algorithm and longitudinally invariant k_t algorithms have a much more robust performance, with a slight advantage for the former.

ATLAS Jet Reconstruction, Calibration, and Tagging of Lorentzboosted Objects

Jet reconstruction in the ATLAS detector takes multiple forms, as motivated by the intended usage of the jet. Different jet definitions are used in particular for the study of QCD jets and jets containing the hadronic decay of boosted massive particles. These different types of jets are calibrated through a series of mostly sequential steps, providing excellent uncertainties, including a first in situ calibration of the jet mass scale. Jet tagging is investigated, including both not-top-quark vs gluon discrimination as well as W/Z boson, H → bb, and top-quark identification. This includes a first look at the use of Boosted Decision Trees and Deep Neural Networks built from jet substructure variables, as well as Convolutional Neural Networks built from jet images. In all cases, these advanced techniques are seen to provide gains over the standard approaches, with the magnitude of the gain depending on the use case. Future methods for improving jet tagging are briefly discussed, including jet substructure-oriented particle flow primarily for W/Z tagging and new subjet reconstruction strategies for H → bb tagging.