Jet Substructure Observables Research Articles

Differential measurements of jet sub-structure observables and their correlations in p + p collisions at s = 200 GeV in STAR

Differential measurements of jet sub-structure observables and their correlations in p + p collisions at s = 200 GeV in STAR

Hard jet substructure in a multistage approach

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the framework. The details of the multistage model and the various parameter choices are described in []. A novel feature of this model is the presence of two stages of jet modification: a high-virtuality phase [modeled using the modular all twist transverse-scattering elastic-drag and radiation model ()], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model ()], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction zg and angular separation rg distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider. Published by the American Physical Society 2024

Measurement of Energy Correlators inside Jets and Determination of the Strong Coupling α_{S}(m_{Z}).

Energy correlators that describe energy-weighted distances between two or three particles in a hadronic jet are measured using an event sample of sqrt[s]=13 TeV proton-proton collisions collected by the CMS experiment and corresponding to an integrated luminosity of 36.3 fb^{-1}. The measured distributions are consistent with the trends in the simulation that reveal two key features of the strong interaction: confinement and asymptotic freedom. By comparing the ratio of the measured three- and two-particle energy correlator distributions with theoretical calculations that resum collinear emissions at approximate next-to-next-to-leading-logarithmic accuracy matched to a next-to-leading-order calculation, the strong coupling is determined at the Z boson mass: α_{S}(m_{Z})=0.1229_{-0.0050}^{+0.0040}, the most precise α_{S}(m_{Z}) value obtained using jet substructure observables.

Infrared-safe energy weighting does not guarantee small nonperturbative effects

Infrared and collinear (IRC) safety has long been used a proxy for robustness when developing new jet substructure observables. This guiding philosophy has been carried into the deep learning era, where IRC-safe neural networks have been used for many jet studies. For graph-based neural networks, the most straightforward way to achieve IRC safety is to weight particle inputs by their energies. However, energy-weighting by itself does not guarantee that perturbative calculations of machine-learned observables will enjoy small nonperturbative corrections. In this paper, we demonstrate the sensitivity of IRC-safe networks to nonperturbative effects, by training an energy flow network (EFN) to maximize its sensitivity to hadronization. We then show how to construct Lipschitz energy flow networks (L-EFNs), which are both IRC safe and relatively insensitive to nonperturbative corrections. We demonstrate the performance of L-EFNs on generated samples of quark and gluon jets, and showcase fascinating differences between the learned latent representations of EFNs and L-EFNs. Published by the American Physical Society 2024

Jet substructure

Jet substructure observables hold the keys to identifying the inner working of the quark–gluon plasma through its imprints in jet modification patterns. Because of the multi-scale nature of jet evolution, the strategy has focused on developing jet reclustering-based and jet shape-based observables to specifically probe different stages of jet evolution, which provide multi-dimensional quantification of jet particle distribution phase spaces. The developments of jet substructure in the past few years allowed us to explore the correlations among jet observables. The richer information beyond single-observable modifications could give provides a crucial step beyond the energy loss paradigm. This review surveys the theory and phenomenology of modern jet substructure observables, which help constraining the energy scales of medium interactions and identifying heavy flavor quarks within jets. Also, the peculiar soft activities point to more refined studies of medium responses to jets.

Radiative corrections for factorized jet observables in heavy ion collisions

I look at the renormalization of the medium structure function and a medium induced jet function in a factorized cross section for jet substructure observables in Heavy Ion collisions. This is based on the formalism developed in [1], which uses an Open quantum system approach combined with the Effective Field Theory (EFT) for forward scattering to derive a factorization formula for jet observables which work as hard probes of a long lived dilute Quark Gluon Plasma (QGP) medium. I show that the universal medium structure function that captures the observable independent physics of the QGP has both rapidity and UV anomalous dimensions that appear due to medium induced Bremsstrahlung. The resulting Renormalization Group (RG) equations correspond to the BFKL equation and the running of the QCD coupling respectively. I present the first results for the numerical impact of resummation using these RG equations on the mean free path of the jet in the medium. I also briefly discuss the prospects of extending this formalism for a short lived dense medium.

Jet substructure observables for jet quenching in quark gluon plasma: A machine learning driven analysis

We present a survey of a comprehensive set of jet substructure observables commonly used to study the modifications of jets resulting from interactions with the Quark Gluon Plasma in Heavy Ion Collisions. The JEWEL event generator is used to produce simulated samples of quenched and unquenched jets. Three distinct analyses using Machine Learning techniques on the jet substructure observables have been performed to identify both linear and non-linear relations between the observables, and to distinguish the Quenched and Unquenched jet samples. We find that most of the observables are highly correlated, and that their information content can be captured by a small set of observables. We also find that the correlations between observables are resilient to quenching effects and that specific pairs of observables exhaust the full sensitivity to quenching effects. The code, the datasets, and instructions on how to reproduce this work are also provided.

Exploring medium properties using jet substructure measurements in pp and Pb–Pb collisions with ALICE

Various studies of jet substructure in heavy-ion collisions offer a consistent picture of QCD medium interactions and a diverse path towards further differentiating energy loss mechanisms. Some results, however, remain disjoint: the jet mass and jet angularities, including girth and thrust, are stronglycorrelated observables which have given seemingly conflicting answers on the angular quenching of jets traversing the quark–gluon plasma (QGP). ALICE has carried out new systematic measurements of these and other perturbativelycalculable angularities using consistent definitions, resolving the girth-mass problem, and revealing quenching effects at broad angles. Concurrently, applying Soft Drop grooming isolates the narrowing in the core of quenched jets. Grooming can also be employed to resolve medium scattering centers, with varying methods used to focus on regions of the splitting phase space. We present the first application of dynamical grooming in heavy-ion collisions to search for excess kT,g emissions as a signature of point-like scatters, providing new constraints on searches for in-medium Molière scattering. We also present a novel shift from studying jet-medium opacity from the angular perspective to a time-like one. By employing a new time reclustering strategy, we potentially enable a time-dependent study of jet substructure observables. We compare all results to assorted jet quenching models, providing new critical information on medium evolution as a function of angular, momentum, and time structure.

Photon-triggered jets as probes of multi-stage jet modification

Prompt photons are created in the early stages of heavy ion collisions and traverse the QGP medium without any interaction. Therefore, photontriggered jets can be used to study the jet quenching in the QGP medium. In this work, photon-triggered jets are studied through different jet and jet substructure observables for different collision systems and energies using the JETSCAPE framework. Since the multistage evolution used in the JETSCAPE framework is adequate to describe a wide range of experimental observables simultaneously using the same parameter tune, we use the same parameters tuned for jet and leading hadron studies. The same isolation criteria used in the experimental analysis are used to identify prompt photons for better comparison. For the first time, high-accuracy JETSCAPE results are compared with multi-energy LHC and RHIC measurements to better understand the deviations observed in prior studies. This study highlights the importance of multistage evolution for the simultaneous description of experimental observables through different collision systems and energies using a single parameter tune.

A simulation study of the soft and hard radiations using jets at the LHC

In this work, different aspects of the high-energy radiation are looked at considering the LHC scenario. An event-shape variable and several jet substructure observables are studied with the Mote Carlo event simulators at the 13 TeV center of mass energy scale to mimic the current LHC environment. The event-shape and the jet substructure observables are chosen such that they are not only sensitive to the different aspects of the high energy radiation measurement but also exhibit promising features to distinguish the possible existence of new physics that considers a dark matter candidate decaying into semi-visible jet. It is verified that the observables exhibit significant sensitivities to disentangle two jets to multi-jet radiations, presence of a final state and initial state radiations, presence of a large amount of missing transverse energy as a strong indication of the possible existence of a dark matter as well as couple of promising features of a semi-visible jet are explored.

Hierarchical high-point Energy Flow Network for jet tagging

Jet substructure observable basis is a systematic and powerful tool for analyzing the internal energy distribution of constituent particles within a jet. In this work, we propose a novel method to insert neural networks into jet substructure basis as a simple yet efficient interpretable IRC-safe deep learning framework to discover discriminative jet observables. The Energy Flow Polynomial (EFP) could be computed with a certain summation order, resulting in a reorganized form which exhibits hierarchical IRC-safety. Thus inserting non-linear functions after the separate summation could significantly extend the scope of IRC-safe jet substructure observables, where neural networks can come into play as an important role. Based on the structure of the simplest class of EFPs which corresponds to path graphs, we propose the Hierarchical Energy Flow Networks and the Local Hierarchical Energy Flow Networks. These two architectures exhibit remarkable discrimination performance on the top tagging dataset and quark-gluon dataset compared to other benchmark algorithms even only utilizing the kinematic information of constituent particles.

A coherent view of the quark-gluon plasma from energy correlators

The ability to measure detailed aspects of the substructure of high-energy jets traversing the quark-gluon plasma (QGP) has provided a new window into its internal dynamics. However, drawing robust conclusions from traditional jet substructure observables has been difficult. In this manuscript we expand on a new approach to jet substructure in heavy-ion collisions based on the study of correlation functions of energy flow operators (energy correlators). We compute the two-point energy correlator of an in-medium massless quark jet and perform a detailed numerical analysis of the produced spectra. Our calculation incorporates vacuum radiation resummed at next-to-leading log accuracy together with the leading order contribution in medium-induced splittings evaluated through the BDMPS-Z multiple scattering and GLV single scattering formalisms for a static brick of QGP. Our analysis demonstrates how particular features of the modifications of in-medium splittings are imprinted in the correlator spectra, particularly showing how energy correlators may be used to extract the onset of colour coherence. We further present a comprehensive discussion on the accuracy and limitations of our study emphasizing how it can be systematically improved. This work sets the foundations for a rich program studying energy correlators in heavy-ion collisions.

Performance and calibration of quark/gluon-jet taggers using 140 fb−1 of pp collisions at TeV with the ATLAS detector* *We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; ANID, Chile; CAS, MOST and NSFC, China; Minciencias, Colombia; MEYS CR, Czech Republic; DNRF and

The identification of jets originating from quarks and gluons, often referred to as quark/gluon tagging, plays an important role in various analyses performed at the Large Hadron Collider, as Standard Model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced background. This paper describes the measurement of the efficiencies of quark/gluon taggers developed within the ATLAS Collaboration, using TeV proton–proton collision data with an integrated luminosity of 140 fb collected by the ATLAS experiment. Two taggers with high performances in rejecting jets from gluon over jets from quarks are studied: one tagger is based on requirements on the number of inner-detector tracks associated with the jet, and the other combines several jet substructure observables using a boosted decision tree. A method is established to determine the quark/gluon fraction in data, by using quark/gluon-enriched subsamples defined by the jet pseudorapidity. Differences in tagging efficiency between data and simulation are provided for jets with transverse momentum between 500 GeV and 2 TeV and for multiple tagger working points.

Unbinned deep learning jet substructure measurement in high Q2ep collisions at HERA

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities. Training these networks was enabled by the use of a large number of GPUs in the Perlmutter supercomputer at Berkeley Lab. The particle jets are reconstructed in the laboratory frame, using the kT jet clustering algorithm. Results are reported at high transverse momentum transfer Q2>150GeV2, and inelasticity 0.2<y<0.7. The analysis is also performed in sub-regions of Q2, thus probing scale dependencies of the substructure variables. The data are compared with a variety of predictions and point towards possible improvements of such models.

CMS pythia 8 colour reconnection tunes based on underlying-event data

New sets of parameter tunes for two of the colour reconnection models, quantum chromodynamics-inspired and gluon-move, implemented in the pythia 8 event generator, are obtained based on the default CMS pythia 8 underlying-event tune, CP5. Measurements sensitive to the underlying event performed by the CMS experiment at centre-of-mass energies sqrt{s}=7 and 13,text {Tehspace{-.08em}V}, and by the CDF experiment at 1.96,text {Tehspace{-.08em}V} are used to constrain the parameters of colour reconnection models and multiple-parton interactions simultaneously. The new colour reconnection tunes are compared with various measurements at 1.96, 7, 8, and 13,text {Tehspace{-.08em}V} including measurements of the underlying-event, strange-particle multiplicities, jet substructure observables, jet shapes, and colour flow in top quark pair ({{text {t}} {}{bar{text {t}}}}) events. The new tunes are also used to estimate the uncertainty related to colour reconnection modelling in the top quark mass measurement using the decay products of {{text {t}} {}{bar{text {t}}}} events in the semileptonic channel at 13,text {Tehspace{-.08em}V}.

Measurements of the groomed jet radius and momentum splitting fraction with the soft drop and dynamical grooming algorithms in pp collisions at sqrt{s} = 5.02 TeV

This article presents measurements of the groomed jet radius and momentum splitting fraction in pp collisions at sqrt{s} = 5.02 TeV with the ALICE detector at the Large Hadron Collider. Inclusive charged-particle jets are reconstructed at midrapidity using the anti-kT algorithm for transverse momentum 60<{p}_{textrm{T}}^{textrm{ch};textrm{jet}}<80 GeV/c. We report results using two different grooming algorithms: soft drop and, for the first time, dynamical grooming. For each grooming algorithm, a variety of grooming settings are used in order to explore the impact of collinear radiation on these jet substructure observables. These results are compared to perturbative calculations that include resummation of large logarithms at all orders in the strong coupling constant. We find good agreement of the theoretical predictions with the data for all grooming settings considered.

Comparing the martini and cujet models for jet quenching: Medium modification of jets and jet substructure

Jets produced by the initial hard scattering in heavy ion collision events lose energy due to interactions with the color-deconfined medium formed around them: The quark-gluon plasma (QGP). Jet-medium interactions constitute an important theoretical and experimental field for studies of QGP, and various models with different assumptions have been proposed to describe them. A fair and direct comparison of these models requires that all other aspects of the simulation be fixed, which is achieved in this work by relying on the jetscape framework. We employ jetscape to directly and comprehensively compare two successful energy loss models: cujet and martini. We compare the models with the results of measurements of jet spectra and substructure observables. With the strong coupling tuned separately, we find that the two models broadly agree with each other in nuclear modification factors for charged hadrons and jets with cone size $R=0.4$. Systematic differences are reported in fragmentation functions, jet shape, and cone-size dependent jet ${R}_{AA}$.

Identification of b jets using QCD-inspired observables

We study the issue of separating hadronic jets that contain bottom quarks ($b$ jets) from jets featuring light partons only. We develop a novel approach to $b$ tagging that exploits the application of QCD-inspired jet substructure observables such as one-dimensional jet angularities and the two-dimensional primary Lund plane. We demonstrate that these observables can be used as inputs to modern machine-learning algorithms to efficiently separate $b$ jets from light ones. In order to test our tagging procedure, we consider simulated events where a $Z$ boson is produced in association with jets and show that using jet angularities as an input for a deep neural network, as well as using images obtained from the primary Lund jet plane as input to a convolutional neural network, one can achieve tagging accuracy comparable with the accuracy of conventional track-based taggers. We argue that the complementary usage of the track-based taggers together with the ones based upon QCD-inspired observables could improve $b$-tagging accuracy.

Learning to identify semi-visible jets

We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substructure observables. Semi-visible jets arise from dark matter particles which decay into a mixture of dark sector (invisible) and Standard Model (visible) particles. Such objects are challenging to identify due to the complex nature of jets and the alignment of the momentum imbalance from the dark particles with the jet axis, but such jets do not yet benefit from the construction of dedicated theoretically-motivated jet substructure observables. A deep network operating on jet constituents is used as a probe of the available information and indicates that classification power not captured by current high-level observables arises primarily from low-pT jet constituents.

Systematic quark/gluon identification with ratios of likelihoods

Discriminating between quark- and gluon-initiated jets has long been a central focus of jet substructure, leading to the introduction of numerous observables and calculations to high perturbative accuracy. At the same time, there have been many attempts to fully exploit the jet radiation pattern using tools from statistics and machine learning. We propose a new approach that combines a deep analytic understanding of jet substructure with the optimality promised by machine learning and statistics. After specifying an approximation to the full emission phase space, we show how to construct the optimal observable for a given classification task. This procedure is demonstrated for the case of quark and gluons jets, where we show how to systematically capture sub-eikonal corrections in the splitting functions, and prove that linear combinations of weighted multiplicity is the optimal observable. In addition to providing a new and powerful framework for systematically improving jet substructure observables, we demonstrate the performance of several quark versus gluon jet tagging observables in parton-level Monte Carlo simulations, and find that they perform at or near the level of a deep neural network classifier. Combined with the rapid recent progress in the development of higher order parton showers, we believe that our approach provides a basis for systematically exploiting subleading effects in jet substructure analyses at the Large Hadron Collider (LHC) and beyond.