Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders. One such method is the Energy Flow Network (EFN) - a recently introduced neural network architecture that represents jets as permutation-invariant sets of particle momenta while maintaining infrared and collinear safety. We develop a variant of the Energy Flow Network architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. We derive conditions under which infrared and collinear safety can be maintained, and study the performance of these networks on the canonical example of W-boson tagging. We find that equivariant Energy Flow Networks have similar performance to Particle Flow Networks, which are superior to standard EFNs. However, equivariant Particle Flow Networks suffer from convergence and overfitting issues. Finally, we study how equivariant networks sculpt the jet mass and provide some initial results on decorrelation using planing.
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.
Searches for invisible Higgs decays in weak boson fusion are a well-known laboratory for jets and QCD studies. We present a series of results on tagging jets and central jet activity. First, precision analyses of the central jet activity require full control of single top production in some analyses. Second, the rate dependence on the size of the tagging jets is not limited to weak boson fusion. For the first time, we show how subjet information on the tagging jets and on the additional jet activity can be used to extract the Higgs signal. The additional observables relieve some of the pressur…
A new jet observable, dipolarity, is introduced that can distinguish whether a pair of subjets arises from a color singlet source. This observable is incorporated into the HEPTopTagger and is shown to improve discrimination between top jets and QCD jets for moderate to high pT.
The signature produced by the Standard Model Higgs boson in the Vector Boson Fusion (VBF) mechanism is usually pinpointed by requiring two well separated hadronic jets, one of which (at least) of them tends to be in the forward direction. With the increase of instantaneous luminosity at the LHC, the isolation of the Higgs boson produced with the VBF mechanism is rendered more challenging. In this paper the feasibility of single jet tagging is explored in a high-luminosity scenario. It is demonstrated that the separation in rapidity between the tagging jet and the Higgs boson can be effectively…
Semivisible jets are a characteristic signature of many confining dark sectors and consist of jets of visible hadrons intermixed with invisible stable particles. Since their initial proposal, considerable progress has been made in developing techniques for tagging anomalous jets. In this paper, we show that the ability to tag semivisible jets can be used to define new event-level variables which use generally ignored kinematic information and can considerably increase our ability to discover dark confining sectors. In practice, our best results are obtained by using the coefficients of the dec…
A method of top tagging is introduced. Using the anti-kt algorithm to define jets, events with nj = 2 fat jets of cone size R = 1.5 are decomposed into R = 0.6 sub-jets and retained if nj (R= 0.6) \geq 4 . One pair of sub-jets reconstructs the W-mass and another jet is tagged as a b-jet, as necessary for hadronic or semileptonic events of tt origin. This 'hollow cone' method distinguishes the tt events from the light parton QCD dijet events. Our simulations are made for the LHC at 7 TeV.
We develop taggers for multi-pronged jets that are simple functions of jet substructure (so-called `subjettiness') variables. These taggers can be approximately decorrelated from the jet mass in a quite simple way. Specifically, we use a Logistic Regression Design (LoRD) which, even being one of the simplest machine learning classifiers, shows a performance which surpasses that of simple variables used by the ATLAS and CMS Collaborations and is not far from more complex models based on neural networks. Contrary to the latter, our method allows for an easy implementation of tagging tasks by pro…
We develop a jet flavour tagger in the contest of electroweak boson production in association with jets. Here the jet with the highest transverse momentum is considered and a simple cut on a jet angularity may serve as a tagger for the flavour of the jet. This is an infrared and collinear (IRC) safe procedure thus it is well-defined from a theoretical viewpoint. Jet angularities exibit a property called Casimir Scaling (CS) at the leading logarithmic (LL) accuracy. Here we consider also the first deviation from the typical CS behaviour because we use the most recent jet angularities calculatio…
The phenomenology of dark sector is complicated if dark sector is charged under a confined hidden gauge group. In such kind of model, a dark parton produced at a high energy collider showers and hadronize to a cluster of dark mesons. Dark mesons then decay to visible particles and produce a jet-like signal, which is called "dark jet" in this work. Collider signal of dark jet depends on the property of dark mesons. For example, a finite lifetime of dark meson would provide displaced vertex or displaced track, thus one can use these displaced objects to tag dark jet. However if the lifetime of d…
We present a theoretical review of the possibilities offered by heavy flavour tagging in testing QCD features in 4-jet events at LEP1. Attention is focused mainly on the properties of the angular variables which are used in the experimental analyses to measure the colour factors of the strong interactions. We also comment on a possible settlement of the ongoing controversy about the existence in LEP1 data of SUSY events involving light gluinos. Integrated and differential rates of interest to phenomenological analyses are given and various tagging procedures are discussed.
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,…
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…
We construct a procedure to separate boosted Higgs bosons decaying into hadrons, from the background due to strong interactions. We employ the Lund jet plane to obtain a theoretically well-motivated representation of the jets of interest and we use the resulting images as the input to a convolutional neural network classifier. In particular, we consider two different decay modes of the Higgs boson, namely into a pair of bottom quarks or into light jets, against the respective backgrounds. For each case, we consider both a moderate- and high- boost scenario. The performance of the tagger is com…
Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has been shown to be an important factor for improving the performance of deep learning in many applications, Lorentz group equivariance - a fundamental spacetime symmetry for elementary particles - has recently been incorporated into a deep learning model for jet tagging. However, the design is computationally costly due to the analytic construction of high-order tensors. In this article, we introduce LorentzNet, a new symmetry-preserving deep learning model for jet taggi…
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…
Despite no new physics so far at the LHC, a $Z'$ boson with $m_{Z'} \sim 100$ GeV could still emerge via Drell-Yan (DY) production, $q \bar q \to Z' \to μ^+ μ^-$, in the next few years. To unravel the nature of the $Z'$ coupling, we utilize the $c$- and $b$-tagging algorithms developed by ATLAS and CMS to investigate $cg \to c Z'$ at 14 TeV LHC. While light-jet contamination can be eliminated, mistagged $b$-jets cannot be rejected in any of the tagging schemes we adopt. On the other hand, for nonzero $bbZ'$ coupling, far superior $b$-tagging could discover the $bg \to b Z'$ process, where agai…
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 scheme for tagging high-$p_{T}$ bottom and charm jets using energetic muons. Contemporary track-based $b$ tags lose their ability to reject light jet background as jet $p_{T}\rightarrow\mathcal{O}(\mathrm{TeV})$, where the massive boost exposes fundamental limits in tracking resolution. For our "$μ_{x}$" tag, the signal efficiency and light jet rejection is robust versus $p_{T}$. In the tested regime (jet $p_{T}\in[\mathrm{0.5,2.1}]$ TeV), $μ_{x}$ tags $\sim14\%$ of $b$ jets, $\sim6.5\%$ of $c$ jets and $\sim0.65\%$ of light jets. Since $μ_{x}$ tagging should be immediately us…
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.
Convolutional neural networks are basic structures using jet images as input for the jet tagging problems. However, what they have learned during the training process is always difficult to understand just through feature maps. Inspired by the attention mechanism popular in machine learning fields, we propose a novel attention-based neural network (ABNN) to get insight of this problem. The ABNN combines a jet image with average jet images from the signal and the background to generate attention maps which show clearly the relevant importance according to the different origination of jets. Comp…
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Princeton University（普林斯顿大学）；University of California Berkeley（加州大学伯克利分校）
Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters. However, real-world applications, such as in high energy physics have not born this out. We present the novel architecture VecNet that combines both symmetry-respecting and unconstrained operations to study and tune the degree of physics-informed GNNs. We introduce a novel metric, the \textit{ant factor}, to quantify the resource-efficiency of each configuration in the search-space. We find that a generalized ar…
Weak boson fusion promises to be a copious source of intermediate mass Standard Model Higgs bosons at the LHC. The additional very energetic forward jets in these events provide for powerful background suppression tools. We analyze the H -> W^(*)W^(*) -> e mu pTmiss decay mode for a Higgs boson mass in the 130-200 GeV range. A parton level analysis of the dominant backgrounds (production of W pairs, tt~ and Z -> tau tau in association with jets) demonstrates that this channel allows the observation of H -> W^(*)W^(*) in a virtually background-free environment, yielding a significant Higgs boso…
We propose a method to probe the coupling of the Higgs to strange quarks by tagging strange jets at future lepton colliders. For this purpose we describe a jet-flavor observable, $J_F$, that is correlated with the flavor of the quark associated with the hard part of the jet. Using this variable, we set up a strangeness tagger aimed at studying the decay $h\to s\bar{s}$. We determine the sensitivity of our method to the strange Yukawa coupling, and find it to be of the order of the standard-model expectation.
Heavy flavour jet tagging is widely used in the determination of cross sections including the production of heavy flavoured quarks. This requires the knowledge of heavy and light flavour jet tagging efficiencies and their uncertainties. A system of eight non-linear equations can be used to determine these quantities by means of two different tagging algorithms and two data sets which differ in heavy flavour jet content. The analytical solution of this system of equations, derived by means of resultants is described in detail, including a discussion about its singularities which provide importa…
The classification of jets induced by quarks or gluons is important for New Physics searches at high-energy colliders. However, available taggers usually rely on modelling the data through Monte Carlo simulations, which could veil intractable theoretical and systematical uncertainties. To significantly reduce biases, we propose an unsupervised learning algorithm that, given a sample of jets, can learn the SoftDrop Poissonian rates for quark- and gluon-initiated jets and their fractions. We extract the Maximum Likelihood Estimates for the mixture parameters and the posterior probability over th…
We consider the process $p p \to t {\bar t} H$. This process can give rise to many signatures of the Higgs boson. The signatures can have electrons, muons and jets. We consider the signatures that have two electrons/muons and jets. Tagging of a tau jet and a bottom jet can help reduce the backgrounds significantly. In particular, we examine the usefulness of the signatures "isolated 2 electrons/muons + a bottom jet + a tau jet", "isolated 2 electrons/muons + 2 tau jets", "isolated 2 electrons/muons + 2 bottom jets + a tau jet", and "isolated 2 electrons/muons + a bottom jet + 2 tau jets". We f…
University of California, Santa Barbara（加州大学圣巴巴拉分校）；CERN（欧洲核子研究组织）
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such a particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph Convolutional Neural Network for jet tagging problems. The ParticleNet arc…
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…
Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.
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…
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 …
We consider the factorized groomed jet mass distribution in inclusive jet processes using modified mass drop tagger (mMDT), corresponding to soft drop with the angular exponent $β=0$. A grooming procedure is implemented rather than tagging in the sense that grooming always returns a groomed jet, while tagging dose not return a jet when a single particle remains after tagging. We find that the grooming procedure makes the jet mass distribution infrared safe and only ultraviolet divergences appear in each factorized part. The groomed jet mass distributions are investigated in a wide range of the…
We analyze the impact of using $b$-tagged samples in studying non-Abelian effects due to QCD in $e^+e^-\ar 4$jet events at $\sqrt s=M_{Z^0}$, using angular variable analyses and comparisons with $e^+e^-\ar 3 \mbox{jet}γ$ events. We find that QCD effects are largely enhanced in $b$-quark samples with respect to `unflavoured' ones, where energy-ordering is used to distinguish between gluon and quark jets. We show that the $b$-quark mass influences the angular distributions significantly and should not be neglected
Jet flavour identification algorithms are of paramount importance to maximise the physics potential of future collider experiments. This work describes a novel set of tools allowing for a realistic simulation and reconstruction of particle level observables that are necessary ingredients to jet flavour identification. An algorithm for reconstructing the track parameters and covariance matrix of charged particles for an arbitrary tracking sub-detector geometries has been developed. Additional modules allowing for particle identification using time-of-flight and ionizing energy loss information …
We investigate the charged Higgs boson signal at the LHC using its dominant production and decay modes with triple b-tagging, i.e. $tH^- \to t\bar tb \to b\bar bb W^+ W^-$, followed by leptonic decay of one W and hadronic decay of the other. We consider the continuum background from the associated production of $t\bar t$ with a b- or a light quark or gluon jet, which can be mis-tagged as b-jet. We reconstruct the top quark masses to identify the 3rd b-jet accompanying the $t\bar t$ pair, and use its p_T distribution to distinguish the signal from the background. Combining this with the reconst…
We present a new tagger which aims at identifying partially reconstructed objects, in which only some of the constituents are collected in a single jet. As an example, we focus on top decays in which either part of the hadronically decaying W or the b jet is soft or falls outside of the top jet cone. We construct an observable to identify remnant substructure from the decay and employ aggressive jet grooming to reject QCD backgrounds. The tagger is complementary to existing ones and works well in the intermediate boost regime where jet substructure techniques usually fail. It is anticipated th…
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 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 …
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…
UCLouvain（瓦隆天主教大学）；Universität Heidelberg（海德堡大学）；Tsung-Dao Lee Institute & School of Physics and Astronomy, Shanghai Jiao Tong University（上海交大李政道研究所与物理与天文学院）；Shanghai Jiao Tong University（上海交通大学）；Durham University（杜伦大学）
The separation of quark and gluon initiated jets can be an important way to improve the sensitivity in searches for new physics or in measurements of Higgs boson properties. We present a simplified version of the shower deconstruction approach as a novel observable for quark-gluon tagging. Assuming topocluster-like objects as input, we compare our observable with energy correlation functions and find a favorable performance for a large variety of jet definitions. When applied to dark matter searches in mono-jet final states limitations from small signal-to-background ratios can be overcome. We…
$N$-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…
The main parts of the LHC diffractive physics programme possible to be measured using a proton tagging technique are presented. The geometric acceptance of the ATLAS forward proton detectors: ALFA and AFP for various LHC optics settings are shown. The probabilities of observing a proton originating from a minimum-bias event in ALFA and AFP stations are given. The main properties of single diffractive and double Pomeron exchange production of dijets, photon+jet, jet-gap-jet and W/Z bosons are discussed. The possibility of measuring the jet production in exclusive (double proton tag) and semi-ex…
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-i…
