Jet identification tools are crucial for new physics searches at the LHC and at future colliders. We introduce the concept of Mass Unspecific Supervised Tagging (MUST) which relies on considering both jet mass and transverse momentum varying over wide ranges as input variables - together with jet substructure observables - of a multivariate tool. This approach not only provides a single efficient tagger for arbitrary ranges of jet mass and transverse momentum, but also an optimal solution for the mass correlation problem inherent to current taggers. By training neural networks, we build MUST-inspired generic and multi-pronged jet taggers which, when tested with various new physics signals, clearly outperform the variables commonly used by experiments to discriminate signal from background. These taggers are also efficient to spot signals for which they have not been trained. Taggers can also be built to determine, with a high degree of confidence, the prongness of a jet, which would be of utmost importance in case a new physics signal is discovered.
We introduce a novel anomaly search method based on (i) jet tagging to select interesting events, which are less likely to be produced by background processes; (ii) comparison of the untagged and tagged samples to single out features (such as bumps produced by the decay of new particles) in the latter. We demonstrate the usefulness of this method by applying it to a final state with two massive boosted jets: for the new physics benchmarks considered, the signal significance increases an order of magnitude, up to a factor of 40. We compare to other anomaly detection methods in the literature an…
We address the modeling dependence of jet taggers built using the method of Mass Unspecific Supervised Tagging, by using two different parton showering and hadronisation schemes. We find that the modeling dependence of the results - estimated by using different schemes in the design of the taggers and applying them to the same type of data - is rather small, even if the jet substructure varies significantly between the two schemes. These results add great value to the use of generic supervised taggers for new physics searches.
We present a new technique for tagging heavy-flavor jets with p_T > 500 GeV called "mu_x tagging." Current track-based methods of b-jet tagging lose efficiency and experience a large rise in fake rate in the boosted regime. Using muons from B hadron decay, we combine angular information and jet substructure to tag b jets, c jets, light jets, and "light-heavy" jets (those containing B hadrons from gluon splitting). We find tagging efficiencies of epsilon_b = 14%, epsilon_c = 6.5%, epsilon_{light-light} = 0.14%, and epsilon_{light-heavy} = 0.5%, respectively, that are nearly independent of trans…
I present a new scheme for tagging boosted heavy flavor jets called "$μ_x$ tagging" and its application to TeV-scale physics beyond the Standard Model. Using muons from B hadron decay to define a particular combination "x" of angular information, and jet substructure variables, we identify a clean ($ε_{\mathrm{fake}}/ε_b\sim 1/100$) good efficiency ($ε_b = 14\%$) tag. I demonstrate the usefulness of this new scheme by showing the reach for discovery of leptophobic $Z^\prime\to b\bar b$ and $tH^\pm\to t t b$.
We present a new technique for distinguishing the hadronic decays of boosted heavy particles from QCD backgrounds based on wavelet transforms. As an initial exploration, we illustrate the technique in the particular case of hadronic $W$ boson decays, comparing it to the ``mass drop'' cut currently used by the LHC experiments. We apply wavelet cuts, which make use of complementary information, and in combination with the mass drop cut results in an improvement of $\sim$7% in discovery reach of hadronic $W$ boson final states over a wide range of transverse momenta.
We initiate the study of the time substructure of jets, motivated by the fact that the next generation of detectors at particle colliders will resolve the time scale over which jet constituents arrive. This effect is directly related to hadronization, which transforms partons into massive hadrons with a distribution of velocities. We review the basic predictions for the velocity distribution of jet hadrons, and suggest an application for this information in the context of boosted object tagging. By noting that the velocity distribution is determined by the properties of the color string which …
We study a class of realizations of the Next-to-Minimal Supersymmetric Standard Model that is motivated by dark matter and Higgs data, and in which the lightest pseudoscalar Higgs boson mass is smaller than twice the bottom quark mass and greater than twice the tau lepton mass. In such scenarios, the lightest pseudoscalar Higgs boson can be copiously produced at the LHC from the decay of heavier superpartners and will dominantly further decay into a pair of tau leptons that is generally boosted. We make use of a boosted object tagging technique designed to tag such a ditau jet, and estimate th…
The impact of event colour structure on the performance of the Johns-Hopkins, CMS, HEPToptagger and N-Subjettiness algorithms is investigated by studying colour singlet and colour octet resonances decaying to top-quark pairs. Large differences in top-tagging efficiency are observed due to the different colour charge of each resonance. These differences are quantified as a function of the algorithm parameters, the jet size parameter and the probability to misidentify light quarks and gluons as top candidates. We suggest that future experimental searches would benefit from optimising the choice …
Top taggers which identify and reconstruct boosted top quarks have been established as novel tools for a multitude of LHC analyses. We show how single top production in association with a light-flavor or bottom jet can be observed in the boosted phase space regime. The full top reconstruction as part of the tagging algorithm allows us to define a distinctive kinematic angle which clearly separates different single top production processes.
We briefly review common tools and methods to identify boosted, hadronically decaying top quarks at the LHC experiments. This includes generic jet substructure variables, specific top identification algorithms, and recent developments in deep learning techniques.
We demonstrate that the classification of boosted, hadronically-decaying weak gauge bosons can be significantly improved over traditional cut-based and BDT-based methods using deep learning and the jet charge variable. We construct binary taggers for $W^+$ vs. $W^-$ and $Z$ vs. $W$ discrimination, as well as an overall ternary classifier for $W^+$/$W^-$/$Z$ discrimination. Besides a simple convolutional neural network (CNN), we also explore a composite of two CNNs, with different numbers of layers in the jet $p_{T}$ and jet charge channels. We find that this novel structure boosts the performa…
A method is proposed for distinguishing highly boosted hadronically decaying W's (W-jets) from QCD-jets using jet substructure. Previous methods, such as the filtering/mass-drop method, can give a factor of ~2 improvement in S/sqrt(B) for jet pT > 200 GeV. In contrast, a multivariate approach including new discriminants such as R-cores, which characterize the shape of the W-jet, subjet planar flow, and grooming-sensitivities is shown to provide a much larger factor of ~5 improvement in S/sqrt(B). For longitudinally polarized W's, such as those coming from many new physics models, the discrimin…
For highly energetic top quarks, the products of the decay t->bqq' are collimated. The 3-prong decay structure can no longer be resolved using calorimeter information alone if the particle jet separation approaches the calorimeter granularity. We propose a new method, the HPTTopTagger, that uses tracks of charged particles inside a fat jet to find top quarks with transverse momentum pT >1 TeV. The tracking information is complemented by the calorimeter measurement of the fat jet energy to eliminate the sensitivity to jet-to-jet fluctuations in the charged-to-neutral particle ratio. We show tha…
Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional neural network (CNN) based technique along with boosted decision trees (BDT). This computer vision approach is also applied to distinguish between left and right polarized top quarks. In this context, an experimentally measurable asymmetry variable is proposed to estimate the polarization. Results indicate that the CNN based classifier is more sensitive to t…
Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a "digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), o…
We introduce a new jet shape -- N-subjettiness -- designed to identify boosted hadronically-decaying objects like electroweak bosons and top quarks. Combined with a jet invariant mass cut, N-subjettiness is an effective discriminating variable for tagging boosted objects and rejecting the background of QCD jets with large invariant mass. In efficiency studies of boosted W bosons and top quarks, we find tagging efficiencies of 30% are achievable with fake rates of 1%. We also consider the discovery potential for new heavy resonances that decay to pairs of boosted objects, and find significant i…
We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.
$N$-subjettiness ratios are in wide use for tagging heavy boosted objects, in particular the ratio of 2-subjettiness to 1-subjettiness for tagging boosted electroweak bosons. In this article we introduce a new, \emph{dichroic} ratio, which uses different regions of a jet to determine the two subjettiness measures, emphasising the hard substructure for the 1-subjettiness and the full colour radiation pattern for the 2-subjettiness. Relative to existing $N$-subjettiness ratios, the dichroic extension, combined with SoftDrop (pre-)grooming, makes it possible to increase the ultimate signal signif…
We investigate the discovery potential of singly produced top-philic resonances at the high luminosity (HL) LHC in the four-top final state. Our analysis spans over the fully-hadronic, semi-leptonic, and same-sign dilepton channels where we present concrete search strategies adequate to a boosted kinematic regime and high jet-multiplicity environments. We utilize the Template Overlap Method (TOM) with newly developed template observables for tagging boosted top quarks, a large-radius jet variable $M_J$ and customized b-tagging tactics for background discrimination. Our results show that the sa…
New TeV-scale physics processes at the LHC can produce Higgs bosons with substantive transverse Lorentz boost, such that the Higgs's decay products are nominally contained in a single jet. In the case of a light Higgs decaying predominantly to bb, previous studies have shown that these Higgs-jets can be identified by capitalizing on jet substructure techniques. In this work, we explore the possibility of also utilizing the subdominant but very distinctive decay h -> tau tau. To this end, we introduce the concept of a ``ditau-jet,'' or a jet consisting of two semi-collinear taus where one or bo…
Studies of the boosted sector in top-quark physics have known a fast-growing development with the arrival of high-energy data at LHC. This short review summarizes the current status of the boosted top-tagging techniques in ATLAS and CMS and presents an overview of some of the most noticeable developments.
A method is introduced for distinguishing top jets (boosted, hadronically decaying top quarks) from light quark and gluon jets using jet substructure. The procedure involves parsing the jet cluster to resolve its subjets, and then imposing kinematic constraints. With this method, light quark or gluon jets with pT ~ 1 TeV can be rejected with an efficiency of around 99% while retaining up to 40% of top jets. This reduces the dijet background to heavy t-tbar resonances by a factor of ~10,000, thereby allowing resonance searches in t-tbar to be extended into the all-hadronic channel. In addition,…
Unveiling the Secrets of New Physics Through Top Quark Tagging
Institute of Physics, Bhubaneswar（布巴内斯瓦尔物理研究所）；Homi Bhabha National Institute（霍米·巴巴国立研究所）；Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；Kaiping Neutrino Research Center（开平中微子研究中心）
本文综述了顶夸克标记在LHC上用于Beyond Standard Model搜索的现状及应用，探讨了基于机器学习的分类方法在区分顶夸克喷注与QCD喷注中的最新进展。
We evaluate the phenomenological applicability of the dynamical grooming technique, introduced in [1], to boosted W and top tagging at LHC conditions. An extension of our method intended for multi-prong decays with an internal mass scale, such as the top quark decay, is presented. First, we tackle the reconstruction of the mass distribution of W and top jets quantifying the smearing due to pileup. When compared to state-of-the-art grooming algorithms like SoftDrop and its recursive version, dynamical grooming shows an enhanced resilience to background fluctuations. In addition, we asses the di…
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 th…
The University of British Columbia（不列颠哥伦比亚大学）；McGill University（麦吉尔大学）；Stanford University（斯坦福大学）
Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet constituent momenta to a fully connected network. This latter approach lends itself well to the use of Recurrent Neural Networks. In this work the applicability of architectures incorporating Long Short-Term Memory (LSTM) networks is explored. Several network architectures, methods …
A feasibility study is presented for the search of the lightest bottom squark (sbottom) in a compressed scenario, where its mass difference from the lightest neutralino is 5 GeV. Two separate studies are performed: $(1)$ final state containing two VBF-like tagging jets, missing transverse energy, and zero or one $b$-tagged jet; and $(2)$ final state consisting of initial state radiation (ISR) jet, missing transverse energy, and at least one $b$-tagged jet. An analysis of the shape of the missing transverse energy distribution for signal and background is performed in each case, leading to sign…
Distinguishing hadronically decaying boosted top quarks from massive QCD jets is an important challenge at the Large Hadron Collider. In this paper we use the power counting method to study jet substructure observables designed for top tagging, and gain insight into their performance. We introduce a powerful new family of discriminants formed from the energy correlation functions which outperform the widely used N-subjettiness. These observables take a highly non-trivial form, demonstrating the importance of a systematic approach to their construction.
We introduce the energy flow polynomials: a complete set of jet substructure observables which form a discrete linear basis for all infrared- and collinear-safe observables. Energy flow polynomials are multiparticle energy correlators with specific angular structures that are a direct consequence of infrared and collinear safety. We establish a powerful graph-theoretic representation of the energy flow polynomials which allows us to design efficient algorithms for their computation. Many common jet observables are exact linear combinations of energy flow polynomials, and we demonstrate the lin…
We study the boosted Higgs tagging using the Lund jet plane. The convolutional neural network is used for the Lund images data set to classify hadronically decaying Higgs from the QCD background. We consider $H\to b \bar{b}$ and $H \to gg$ decay for moderate and high Higgs transverse momentum and compare the performance with the cut based approach using the jet color ring observable. The approach using Lund plane images provides good tagging efficiency for all the cases.
Rudolf Peierls Centre for Theoretical Physics（鲁道夫·派尔斯理论物理中心）；CERN（欧洲核子研究中心）
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 which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study…
At the LHC, tagging boosted heavy particle resonances which decay hadronically, such as top quarks and Higgs bosons, can play an essential role in new physics searches. In events with high multiplicity, however, the standard approach to tag boosted resonances by a large-radius fat jet becomes difficult because the resonances are not well-separated from other hard radiation. In this paper, we propose a different approach to tag and reconstruct boosted resonances by using the recently proposed mass-jump jet algorithm. A key feature of the algorithm is the flexible radius of the jets, which resul…
By representing each collider event as a point cloud, we adopt the Graphic Convolutional Network (GCN) with focal loss to reconstruct the Higgs jet in it. This method provides higher Higgs tagging efficiency and better reconstruction accuracy than the traditional methods which use jet substructure information. The GCN, which is trained on events of the $H$+jets process, is capable of detecting a Higgs jet in events of several different processes, even though the performance degrades when there are boosted heavy particles other than the Higgs in the event. We also demonstrate the signal and bac…
We study resonant pair production of heavy particles in fully hadronic final states by means of jet substructure techniques. We propose a new resonance tagging strategy that smoothly interpolates between the highly boosted and fully resolved regimes, leading to uniform signal efficiencies and background rejection rates across a broad range of masses. Our method makes it possible to efficiently replace independent experimental searches, based on different final state topologies, with a single common analysis. As a case study, we apply our technique to pair production of Higgs bosons decaying in…
Jet tagging has become an essential tool for new physics searches at the high-energy frontier. For jets that contain energetic charged leptons we introduce Feature Extended Supervised Tagging (FEST) which, in addition to jet substructure, considers the features of the charged lepton within the jet. With this method we build dedicated taggers to discriminate among boosted $H \to \ell νq \bar q$, $t \to \ell νb$, and QCD jets (with $\ell$ an electron or muon). The taggers have an impressive performance, allowing for overall light jet rejection factors of $10^4-10^5$, for top quark / Higgs boson …
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 jets including heavy particles (fat jets) can be analyzed on the level of their subjet constituents. LHC data will soon allow us to establish this new analysis method. We discuss different tagging algorithms, their critical QCD aspects, and currently available experimental results. For the development of taggers and their different applications this review sh…
Naturalness considerations, together with the non-observation of superpartners of the Standard Model particles at the Large Hadron Collider (LHC) so far, favor supersymmetric (SUSY) models in which third generation squarks are significantly lighter than those of the first two generations. In such models, gluino pair-production is typically the dominant SUSY production process at the LHC, and it often leads to final states with multiple top quarks. Some of these top quarks may be relativistic in the lab frame, in which case their hadronic decays may produce "top jets". We propose that the recen…
Korea University（高丽大学）；Korea Institute for Advanced Study（韩国高等研究院）；Cornell University（康奈尔大学）
Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging. To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies. In this paper, we construct a top-jet tagger based on a Convolutional Neural Network (CNN), and apply it to parton-level boosted top samples, with and without an additional gluon in the final state. We show that the jet observable defined by the CNN obeys the canonical definition of infrared safety: it is unaffected by the presence of the extra gluon, as long as it is soft or collin…
We present a procedure for tagging boosted semi-leptonic $t\bar t$ events based on the Template Overlap Method. We introduce a new formulation of the template overlap function for leptonically decaying boosted tops and show that it can be used to compensate for the loss of background rejection due to reduction of b-tagging efficiency at high $p_T$. A study of asymmetric top pair production due to higher order effects shows that our approach improves the resolution of the truth level kinematic distributions. We show that the hadronic top overlap is weakly susceptible to pileup up to 50 interact…
We introduce a novel approach to jet tagging and classification through the use of techniques inspired by computer vision. Drawing parallels to the problem of facial recognition in images, we define a jet-image using calorimeter towers as the elements of the image and establish jet-image preprocessing methods. For the jet-image processing step, we develop a discriminant for classifying the jet-images derived using Fisher discriminant analysis. The effectiveness of the technique is shown within the context of identifying boosted hadronic W boson decays with respect to a background of quark- and…
Studies of highly-boosted top quarks produced inclusively in pp collisions at 14 TeV are discussed. The hadronic decays of boosted top quarks was studied in a data-driven approach by analysing shapes of jet-mass distributions. Using Monte Carlo models after a fast detector simulation, it is shown that inclusive production of boosted top quarks can be observed if it has a cross section at least twice larger than the prediction from the approximate next-to-next-to-leading-order (aNNLO) calculation for the ttbar process. The ttbar process with the nominal aNNLO strength can be measured using the …
At the LHC, a generic supersymmetric cascade can be a source of top quark. Specifically third generation squarks and gluino are the major sources of top quark which could also be boosted. In this article, we have shown that jet substructure algorithm can be very useful in identifying such boosted top quarks in the cascade. We take inclusive three jets plus zero lepton plus missing energy final state and try to reconstruct at least one hadronically decaying top quark by using top tagging techniques which has good prospect at the LHC.
Tagging jets of strongly interacting particles initiated by energetic strange quarks is one of the few largely unexplored Standard Model object classification problems remaining in high energy collider physics. In this paper we investigate the purest version of this classification problem in the form of distinguishing strange-quark jets from down-quark jets. Our strategy relies on the fact that a strange-quark jet contains on average a higher ratio of neutral kaon energy to neutral pion energy than does a down-quark jet. Long-lived neutral kaons deposit energy mainly in the hadronic calorimete…
We develop a new method for tagging jets produced by hadronically decaying top quarks. The method is an application of shower deconstruction, a maximum information approach that was previously applied to identifying jets produced by Higgs bosons that decay to bottom quarks. We tag an observed jet as a top jet based on a cut on a calculated variable that is an approximation to the ratio of the likelihood that a top jet would have the structure of the observed jet to the likelihood that a non-top QCD jet would have this structure. We find that the shower deconstruction based tagger can perform b…
N-subjettiness is a jet shape designed to identify boosted hadronic objects such as top quarks. Given N subjet axes within a jet, N-subjettiness sums the angular distances of jet constituents to their nearest subjet axis. Here, we generalize and improve on N-subjettiness by minimizing over all possible subjet directions, using a new variant of the k-means clustering algorithm. On boosted top benchmark samples from the BOOST2010 workshop, we demonstrate that a simple cut on the 3-subjettiness to 2-subjettiness ratio yields 20% (50%) tagging efficiency for a 0.23% (4.1%) fake rate, making N-subj…
