In this paper we study aspects of top tagging from first principles of QCD. We find that the method known as the CMS top tagger becomes collinear unsafe at high $p_t$ and propose variants thereof which are IRC safe, and hence suitable for analytical studies, while giving a comparable performance to the CMS tagger. We also develop new techniques to identify three-pronged jet substructure, based on adaptations of the Y-splitter method and its combination with grooming. A novel feature of our studies, relative to previous calculations of two-pronged substructure, is our use of triple-collinear splitting functions, which owes to the presence of two mass scales of the same order, $m_t$ and $m_W$, in the signal jet. We carry out leading logarithmic resummed calculations for the various top-taggers, for both background and signal jets, and compare the results to those from parton showers. We also identify and comment on the main features driving tagger performance at high $p_t$ and discuss the role of non-perturbative effects.
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.
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…
We study top-tagging from an analytical QCD perspective focusing on the role of two key steps therein : a step to find three-pronged substructure and a step that places constraints on radiation. For the former we use a recently introduced modification of Y-Splitter, known as Y$_{\text{m}}$-Splitter, and for the latter we use the well-known N-subjettiness variable. We derive resummed results for this combination of variables for both signal jets and background jets, also including pre-grooming of the jet. Our results give new insight into the performance of top tagging tools in particular with …
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…
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。
Jet tagging is a crucial classification task in high energy physics. Recently the performance of jet tagging has been significantly improved by the application of deep learning techniques. In this study, we introduce a new architecture for jet tagging: the Particle Dual Attention Transformer (P-DAT). This novel transformer architecture stands out by concurrently capturing both global and local information, while maintaining computational efficiency. Regarding the self attention mechanism, we have extended the established attention mechanism between particles to encompass the attention mechanis…
In this paper, the question of observability of a heavy charged Higgs in the mass range 400 GeV < mH < 1000 GeV, is addressed. The production process is set to pp -> H+ -> tb at 14 TeV LHC. The analysis benefits from top tagging technique which is based on finding a fat jet as a result of the boosted top quark decay in signal events. A detailed hadron level analysis is performed and selection efficiencies are presented with different charged Higgs mass hypotheses. Finally running toy experiments and using pseudo-data, a fit over signal plus background distributions is performed to assess possi…
We study the LHC sensitivity to new broad neutral resonances produced in two-photon fusion and decaying to a top quark pair, $γγ\to t\bar{t}$. This is probed in central exclusive $t\bar{t}$ production in proton-proton collisions, $pp \to p t\bar{t} p$. We use the tagging of the intact protons by PPS (CMS) and AFP (ATLAS) and consider the semi-leptonic $t\bar t$ channel. The sensitivity is also mapped onto a set of dimension-8 $γγt\bar{t}$ operators in the large mass limit. Using the kinematical correlations between the intact protons and the reconstructed $t\bar{t}$ system, we obtain a sensiti…
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.
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…
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 …
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach …
We study the feasibility of probing a region of Natural Supersymmetry where the stop and higgsino masses are compressed. Although this region is most effectively searched for in the mono-jet channel, this signature is present in many other non-supersymmetric frameworks. Therefore, another channel that carries orthogonal information is required to confirm the existence of the light stop and higgsinos. We show that a supersymmetric version of the $t \bar t H$ process, $pp \to t \tilde t_1 \tilde χ^0_{1(2)}$, can have observably large rate when both the stop and higgsinos are significantly light,…
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.
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential approach to this task is taken by using an ordered sequence of jet constituents as training inputs. Unlike the majority of previous approaches, this strategy does not result in a loss of information during pixelisation or the calculation of high level features. The jet classification method achieves a background rejection of 45 at a 50% efficiency operating …
At proposed future hadron colliders and in the coming years at the LHC, top quarks will be produced at genuinely multi-TeV energies. Top-tagging at such high energies forces us to confront several new issues in terms of detector capabilities and jet physics. Here, we explore these issues in the context of some simple JHU/CMS-type declustering algorithms and the N-subjettiness jet-shape variable tau_32. We first highlight the complementarity between the two tagging approaches at particle-level with respect to discriminating top-jets against gluons and quarks, using multivariate optimization sca…
b喷注的识别是研究和表征顶夸克事件等各种通道和许多新物理现象的关键问题。利用b夸克喷注中产生的b强子寿命长、质量大、动量分数大的特点，CMS定义了不同的b强子标记技术。基于b-强子次顶点的测度或具有大碰撞参量的径迹，已经发展了有效的算法。利用2011年7 TeV pp碰撞的数据，估算了轻味喷流的b标记效率和错误率。
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…
The Cabibbo-Kobayashi-Maskawa (CKM) element $V_{ts}$, representing the coupling between the top and strange quarks, is currently best determined through fits based on the unitarity of the CKM matrix, and measured indirectly through box-diagram oscillations, and loop-mediated rare decays of the $B$ or $K$ mesons. It has been previously proposed to use the tree level decay of the $t$ quark to the $s$ quark to determine $|V_{ts}|$ at the LHC, which has become a top factory. In this paper, we extend the proposal by performing a detailed analysis of measuring $t \to sW$ in dileptonic $t\bar{t}$ eve…
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 …
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…
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…
Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface
Center for Theoretical Physics, Sloane Physics Laboratory, Yale University（理论物理中心，斯洛恩物理实验室，耶鲁大学）；III. Physics Institute A, RWTH Aachen University（物理研究所A，亚琛工业大学）
We apply computer vision with deep learning -- in the form of a convolutional neural network (CNN) -- to build a highly effective boosted top tagger. Previous work (the "DeepTop" tagger of Kasieczka et al) has shown that a CNN-based top tagger can achieve comparable performance to state-of-the-art conventional top taggers based on high-level inputs. Here, we introduce a number of improvements to the DeepTop tagger, including architecture, training, image preprocessing, sample size and color pixels. Our final CNN top tagger outperforms BDTs based on high-level inputs by a factor of $\sim 2$--3 …
JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
GREYC, Normandy University, Unicaen, ENSICAEN, UMR CNRS 6072（GREYC，诺曼底大学，Unicaen，ENSICAEN，CNRS UMR 6072）；LPC, Normandy University, Unicaen, ENSICAEN, IN2P3, UMR CNRS 6534（LPC，诺曼底大学，Unicaen，ENSICAEN，IN2P3，CNRS UMR 6534）
Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University（亚琛工业大学理论粒子物理与宇宙学研究所（TTK））
Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches. We scrutinize the usage of autoencoders for unsupervised anomaly detection based on reconstruction loss to show their capabilities, but also their limitations. As a particle physics benchmark scenario, we study the tagging of top jet images in a background of QCD jet images. Although we reproduce the positive results from the literature, we show that the standard autoencoder se…
We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics. The particle convolution layer can be viewed as an extension of Deep Sets and Energy Flow network architectures, in which the permutation-invariant operator is promoted to a group convolution. While the PCN can be implemented for various kinds of symmetries, we consider the specific case of rotation about the jet axis the $η- ϕ$ plane. In two standard benchmark tasks, q/g tagging and top tagging, we show that the rotational PCN (rPCN) achieves performance …
