Quark Jets Research Articles

Binary junipr: An Interpretable Probabilistic Model for Discrimination.

junipr is an approach to unsupervised learning in particle physics that scaffolds a probabilistic model for jets around their representation as binary trees. Separate junipr models can be learned for different event or jet types, then compared and explored for physical insight. The relative probabilities can also be used for discrimination. In this Letter, we show how the training of the separate models can be refined in the context of classification to optimize discrimination power. We refer to this refined approach as binary junipr. binary junipr achieves state-of-the-art performance for quark-gluon discrimination and top tagging. The trained models can then be analyzed to provide physical insight into how the classification is achieved. As examples, we explore differences between quark and gluon jets and between gluon jets generated with two different simulations.

Extracting a short distance top mass with light grooming

We propose a kinematic method based on a factorization formula for precisely measuring the top quark mass $m_t$ in $pp$ collisions using boosted top jets with light soft drop grooming. By using light grooming, which is an order of magnitude less aggressive than typical grooming, we retain a universal description of the top mass scheme and decay effects, while still effectively removing soft contamination from the top jet. We give field theory results for the hadronization corrections for jets induced by a heavy top quark, showing they are described by a universal hadronic parameter that also appears for groomed light quark jets. An important phenomenological application of our results is that one can obtain $m_t$ in a short distance scheme by fitting the hadron level jet mass distributions, predicted by our factorization formula, to data or by Monte-Carlo calibration. The peaked distributions for $pp$ and $e^+e^-$ collisions are similar, up to sensitivity to underlying event which is significantly reduced by soft drop. Since soft drop implies that the $t$ and $\bar t$ jet masses each can be independently measured, the analysis enables the use of lepton+jet samples.

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

A theory of quark vs. gluon discrimination

Understanding jets initiated by quarks and gluons is of fundamental importance in collider physics. Efficient and robust techniques for quark versus gluon jet discrimination have consequences for new physics searches, precision αs studies, parton distribution function extractions, and many other applications. Numerous machine learning analyses have attacked the problem, demonstrating that good performance can be obtained but generally not providing an understanding for what properties of the jets are responsible for that separation power. In this paper, we provide an extensive and detailed analysis of quark versus gluon discrimination from first-principles theoretical calculations. Working in the strongly-ordered soft and collinear limits, we calculate probability distributions for fixed N -body kinematics within jets with up through three resolved emissions left(mathcal{O}left({alpha}_s^3right)right) . This enables explicit calculation of quantities central to machine learning such as the likelihood ratio, the area under the receiver operating characteristic curve, and reducibility factors within a well-defined approximation scheme. Further, we relate the existence of a consistent power counting procedure for discrimination to ideas for operational flavor definitions, and we use this relationship to construct a power counting for quark versus gluon discrimination as an expansion in {e}^{C_F-{C}_A}ll 1 , the exponential of the fundamental and adjoint Casimirs. Our calculations provide insight into the discrimination performance of particle multiplicity and show how observables sensitive to all emissions in a jet are optimal. We compare our predictions to the performance of individual observables and neural networks with parton shower event generators, validating that our predictions describe the features identified by machine learning.

Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum

Results are reported from a search for supersymmetric particles in the final state with multiple jets and large missing transverse momentum. The search uses a sample of proton-proton collisions at sqrt{s} = 13 TeV collected with the CMS detector in 2016–2018, corresponding to an integrated luminosity of 137 fb−1, representing essentially the full LHC Run 2 data sample. The analysis is performed in a four-dimensional search region defined in terms of the number of jets, the number of tagged bottom quark jets, the scalar sum of jet transverse momenta, and the magnitude of the vector sum of jet transverse momenta. No significant excess in the event yield is observed relative to the expected background contributions from standard model processes. Limits on the pair production of gluinos and squarks are obtained in the framework of simplified models for supersymmetric particle production and decay processes. Assuming the lightest supersymmetric particle to be a neutralino, lower limits on the gluino mass as large as 2000 to 2310 GeV are obtained at 95% confidence level, while lower limits on the squark mass as large as 1190 to 1630 GeV are obtained, depending on the production scenario.

Transverse parton distribution and fragmentation functions at NNLO: the quark case

We revisit the calculation of perturbative quark transverse momentum de- pendent parton distribution functions and fragmentation functions using the exponential regulator for rapidity divergences. We show that the exponential regulator provides a consistent framework for the calculation of various ingredients in transverse momentum dependent factorization. Compared to existing regulators in the literature, the exponential regulator has a couple of advantages which we explain in detail. As a result, the calcula- tion is greatly simplified and we are able to obtain the next-to-next-to-leading order results up to O(E2) in dimensional regularization. These terms are necessary for a higher order calculation which is made possible with the simplification brought by the new regulator. As a by-product, we have obtained the two-loop quark jet function for the Energy-Energy Correlator in the back-to-back limit, which is the last missing ingredient for its N3LL resummation.

Gluon emission from heavy quarks in dense nuclear matter

We study the medium-induced gluon emission process experienced by a hard jet parton propagating through the dense nuclear matter in the framework of deep inelastic scattering off a large nucleus. We work beyond the collinear rescattering expansion and the soft gluon emission limit, and derive a closed formula for the medium-induced single gluon emission spectrum from a heavy or light quark jet interacting with the dense nuclear medium via transverse and longitudinal scatterings. Without performing the collinear rescattering expansion, the medium-induced gluon emission spectrum is controlled by the full distribution of the differential elastic scattering rates between the propagating partons and the medium constituents. We further show that if one utilizes heavy static scattering centers for the traversed nuclear matter and takes the soft gluon emission limit, our result can reduce to the first order in opacity Djordjevic-Gyulassy-Levai-Vitev formula.

Predictions for energy correlators probing substructure of groomed heavy quark jets

We develop an effective field theory (EFT) framework to perform an analytic calculation for energy correlator observables computed on groomed heavy-quark jets. A soft-drop grooming algorithm is applied to a jet initiated by a massive quark to minimize soft contamination effects such as pile-up and multi-parton interactions. We specifically consider the two-particle energy correlator as an initial application of this EFT framework to compute heavy quark jet substructure. We find that there are different regimes for the event shapes, depending on the size of the measured correlator observable, that require the use of different EFT formulations, in which the quark mass and grooming parameters may be relevant or not. We use the EFT to resum large logarithms in the energy correlator observable in terms of the momentum of a reconstructed heavy hadron to NLL′ accuracy and subsequently match it to a full QCD mathcal{O} (αs) cross section, which we also compute. We compare our predictions to simulations in Pythia for e+e− collisions. We find a good agreement with partonic simulations, as well as hadronic ones with an appropriate shape function used to describe nonperturbative effects and the heavy quark hadron decay turned off. We also predict the scaling behavior for the leading nonperturbative power correction due to hadronization. Consequently, we can give a prediction for the energy correlator distribution at the level of the reconstructed heavy hadron. This work provides a general framework for the analysis of heavy quark jet substructure observables.

Lepton angular distribution of Z boson production and jet discrimination

High precision data of lepton angular distributions in inclusive Z boson production, reported by the CMS and ATLAS Collaborations, showed pronounced transverse momentum (qT) dependencies of the A0 and A2 coefficients. Violation of the Lam-Tung relation, A0=A2, was also found. An intuitive understanding of these results can be obtained from a geometric approach. We predict that A0 and A2 for Z plus single gluon-jet events are very different from that of Z plus single quark-jet events, allowing a new experimental tool for checking various algorithms which attempt to discriminate quark jets from gluon jets. We also predict that the Lam-Tung relation would be more severely violated for the Z plus multiple-jet data than what has been observed so far for inclusive Z production data. These predictions can be readily tested using existing LHC data.

Measurement of jet-substructure observables in top quark, W boson and light jet production in proton-proton collisions at sqrt{s} = 13 TeV with the ATLAS detector

A measurement of jet substructure observables is presented using data collected in 2016 by the ATLAS experiment at the LHC with proton-proton collisions at sqrt{s} = 13 TeV. Large-radius jets groomed with the trimming and soft-drop algorithms are studied. Dedicated event selections are used to study jets produced by light quarks or gluons, and hadronically decaying top quarks and W bosons. The observables measured are sensitive to substructure, and therefore are typically used for tagging large-radius jets from boosted massive particles. These include the energy correlation functions and the N-subjettiness variables. The number of subjets and the Les Houches angularity are also considered. The distributions of the substructure variables, corrected for detector effects, are compared to the predictions of various Monte Carlo event generators. They are also compared between the large-radius jets originating from light quarks or gluons, and hadronically decaying top quarks and W bosons.

Two-loop massive quark jet functions in SCET

We calculate the O( {alpha}_s^2 ) corrections to the primary massive quark jet functions in Soft-Collinear Effective Theory (SCET). They are an important ingredient in factorized predictions for inclusive jet mass cross sections initiated by massive quarks emerging from a hard interaction with smooth quark mass dependence. Due to the effects coming from the secondary production of massive quark-antiquark pairs there are two options to define the SCET jet function, which we call universal and mass mode jet functions. They are related to whether or not a soft mass mode (zero) bin subtraction is applied for the secondary massive quark contributions and differ in particular concerning the infrared behavior for vanishing quark mass. We advocate that a useful alternative to the common zero-bin subtraction concept is to define the SCET jet functions through subtractions related to collinear-soft matrix elements. This avoids the need to impose additional power counting arguments as required for zero-bin subtractions. We demonstrate how the two SCET jet function definitions may be used in the context of two recently developed factorization approaches to treat secondary massive quark effects. We clarify the relation between these approaches and in which way they are equivalent. Our two-loop calculation involves interesting technical subtleties related to spurious rapidity divergences and infrared regularization in the presence of massive quarks.

Collinear limit of the energy-energy correlator

The energy-energy-correlator (EEC) observable in $e^+e^-$ annihilation measures the energy deposited in two detectors as a function of the angle between the detectors. The collinear limit, where the angle between the two detectors approaches zero, is of particular interest for describing the substructure of jets produced at hadron colliders as well as in $e^+e^-$ annihilation. We derive a factorization formula for the leading power asymptotic behavior in the collinear limit of a generic quantum field theory, which allows for the resummation of logarithmically enhanced terms to all orders by renormalization group evolution. The relevant anomalous dimensions are expressed in terms of the timelike data of the theory, in particular the moments of the timelike splitting functions, which are known to high perturbative orders. We relate the small angle and back-to-back limits to each other via the total cross section and an integral over intermediate angles. This relation provides us with the initial conditions for quark and gluon jet functions at order $\alpha_s^2$. In QCD and in $\mathcal{N}=1$ super-Yang-Mills theory, we then perform the resummation to next-to-next-to-leading logarithm, improving previous calculations by two perturbative orders. We highlight the important role played by the non-vanishing $\beta$ function in these theories, which while subdominant for Higgs decays to gluons, dominates the behavior of the EEC in the collinear limit for $e^+e^-$ annihilation, and in $\mathcal{N}=1$ super-Yang-Mills theory. In conformally invariant $\mathcal{N}=4$ super-Yang-Mills theory, reciprocity between timelike and spacelike evolution can be used to express our factorization formula as a power law with exponent equal to the spacelike twist-two spin-three anomalous dimensions, thus providing a connection between timelike and spacelike approaches.

Higher-order corrections to heavy-quark jet quenching

We calculate higher-order corrections to the quenching factor of heavy-quark jets due to hard, in-medium splittings in the framework of the BDMPS-Z formalism. These corrections turn out to be sensitive to a single mass-scale sim ({hat{q}} L)^{{nicefrac {1}{2}}}, where {hat{q}} is the medium transport coefficient and L the path length, and allow to draw a distinction between the way light, with m < ({hat{q}} L)^{1/2} (in contrast to massless m=0), and genuinely heavy, with m > ({hat{q}} L)^{1/2}, quark jets are quenched in the medium. We show that the corrections to the quenching factor at high energies are double-logarithmic and qualitatively of the same order as for the massless quark jet.

Quark jet versus gluon jet: fully-connected neural networks with high-level features

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 physics input is required. Inspired by a recent work by Datta and Larkoski, we study the classification of quark/gluon-initiated jets based on fully-connected neural networks (FNNs), where expert-designed physical variables are taken as input. FNNs are applied in two ways: trained separately on various narrow jet transverse momentum $p_{TJ}$ bins; trained on a wide region of $p_{TJ} \in [200,~1000]$ GeV. We find their performances are almost the same. The performance is better when the $p_{TJ}$ is larger. Jet discrimination with FNN is studied on both particle and detector level data. The results based on particle level data are comparable with those from deep convolutional neural networks, while the significance improvement characteristic (SIC) from detector level data would at most decrease by $15\%$. We also test the performance of FNNs with full set or subsets of jet observables as input features. The FNN with one subset consisting of fourteen observables shows nearly no degradation of performance. This indicates that these fourteen expert-designed observables could have captured the most necessary information for separating quark and gluon jets.

Factorization of Jet Cross Sections in Heavy-Ion Collisions.

We propose a new phenomenological approach to establish QCD factorization of jet cross sections in the heavy-ion environment. Starting from a factorization formalism in proton-proton collisions, we introduce medium modified jet functions to capture the leading interaction of jets with the hot and dense QCD medium. A global analysis using a MonteCarlo sampling approach is performed in order to reliably determine the new jet functions from the nuclear modification factor of inclusive jets at the LHC. We find that gluon jets are significantly more suppressed due to the presence of the medium than quark jets. In addition, we observe that the jet radius dependence is directly related to the relative suppression of quark and gluon jets. Our approach may help to improve the extraction of medium properties from data.

Quark jet rates and quark-gluon discrimination in multijet final states

We estimate the number of quark jets in QCD multijet final states at hadron colliders. In the estimation, we develop the calculation of jet rates into that of quark jet rates. From the calculation, we estimate the improvement on the signal-to-background ratio for a signal semianalytically by applying quark-gluon discrimination, where the signal predicts many quark jets. We introduce a variable related to jet flavors in multijet final states and propose a data-driven method using the variable. As with the semianalytical result, the improvements in the signal-to-background ratio using the variable in Monte Carlo analysis are estimated.

Investigating the topology dependence of quark and gluon jets

As most target final states for searches and measurements at the Large Hadron Collider have a particular quark/gluon composition, tools for distinguishing quark- from gluon-initiated jets can be very powerful. In addition to the difficulty of the classification task, quark-versus-gluon jet tagging is challenging to calibrate. The difficulty arises from the topology dependence of quark-versus-gluon jet tagging: since quarks and gluons have net quantum chromodynamic color charge while only colorless hadrons are measured, the radiation pattern inside a jet of a particular type depends on the rest of its environment. Given a definition of a quark or gluon jet, this paper studies the topology dependence of such jets in simulation. A set of phase space regions and jet substructure observables are identified for further comparative studies between generators and eventually in data.

Probing heavy ion collisions using quark and gluon jet substructure with machine learning

Understanding the inner working of the quark-gluon plasma requires complete and precise jet substructure studies in heavy ion collisions. In this proceeding we discuss the use of quark and gluon jets as independent probes, and how their classification allows us to uncover regions of QCD phase space sensitive to medium dynamics. We introduce the telescoping deconstruction (TD) framework to capture complete jet information and show that TD observables reveal fundamental properties of quark and gluon jets and their modifications in the medium. We draw connections to soft-drop subjet distributions and illuminate medium-induced jet modifications using Lund diagrams. The classification is also studied using a physics-motivated, multivariate analysis of jet substructure observables. Moreover, we apply image-recognition techniques by training a deep convolutional neural network on jet images to benchmark classification performances. We find that the quark gluon discrimination performance worsens in Jewel-simulated heavy ion collisions due to significant soft radiation affecting soft jet substructures. This work suggests a systematic framework for jet studies and facilitates direct comparisons between theoretical calculations and measurements in heavy ion collisions.

Gluon Spectrum to Any Order in Opacity for Jets at the EIC

Jets produced at a future electron-ion collider (EIC) are predominantly initiated by energetic quarks, and the subsequent branching of these quarks into systems of quarks and gluons is heavily modified by multiple scattering in the nuclear medium. In cold nuclear matter and in the quark-gluon plasma, this feature allows jet cross sections and jet substructure to be used as a probe of the medium's properties. Here we present a formalism for computing the gluon substructure of a quark jet to an arbitrary order in opacity, the average number of scatterings in the medium, while maintaining the exact kinematics of the splitting. We present explicit results at second order in opacity for the first time, along with a recursion relation which can be used to construct the solution for arbitrarily high orders in opacity. The theoretical framework demonstrated here applies equally well to light and heavy quark branching, and is easily generalizable to other systems.

Scale Breaking in Quark and Gluon Jets in Electron Positron Annihilations, using AMY Data

We have studied hadronic events for e+e− annihilation by using real AMY data as well as Monte Carlo simulated events. We present mean cone angle and transverse momentum for quark and gluon jets. We observe that the cone angle for gluon jet sample is wider than that for quark jet. Transverse momentum with respect to the jet axis is also higher for gluon jet. Next we study scaling violation for two kinds of jets. Due to the higher color factor for gluon jet, it shows a stronger scaling violation compared to quark jet. A good agreement is found between measurements and corresponding QCD predictions. We will explain all these features in this paper.