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Register a new userIdentifying a new particle with jet substructures
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We investigate a potential of measuring properties of a heavy resonance X, exploiting jet substructure techniques. Motivated by heavy higgs boson searches, we focus on the decays of X into a pair of (massive) electroweak gauge bosons. More specifically, we consider a hadronic Z boson, which makes it possible to determine properties of X at an earlier stage. For $m_X$ of O(1) TeV, two quarks from a Z boson would be captured as a merged jet in a significant fraction of events. The use of the merged jet enables us to consider a Z-induced jet as a reconstructed object without any combinatorial ambiguity. We apply a conventional jet substructure method to extract four-momenta of subjets from a merged jet. We find that jet substructure procedures may enhance features in some kinematic observables formed with subjets. Subjet momenta are fed into the matrix element associated with a given hypothesis on the nature of X, which is further processed to construct a matrix element method (MEM)-based observable. For both moderately and highly boosted Z bosons, we demonstrate that the MEM with current jet substructure techniques can be a very powerful discriminator in identifying the physics nature of X. We also discuss effects from choosing different jet sizes for merged jets and jet-grooming parameters upon the MEM analyses.
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The development of techniques for identifying hadronic signals from the overwhelming multi-jet backgrounds is an important part of the Large Hadron Collider (LHC) program. Of prime importance are resonances decaying into a pair of partons, such as the Higgs and $rm W$/$rm Z$ bosons, as well as hypothetical new particles. We present a simple observable to help discriminate a dijet resonance from background that is effective even when the decaying resonance is not strongly boosted. We find consistent performance of the observable over a variety of processes and degree of boosts, and show that it leads to a reduction of the background by a factor of $3-5$ relative to signal at the price of $10-20%$ signal efficiency. This approach represents a significant increase in sensitivity for Standard Model (SM) measurements and searches for new physics that are dominated by systematic uncertainties, which is true of many analyses involving jets - particularly in the high-luminosity running of the LHC.
We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element - parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space. We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques from statistics, machine learning, and combinatorial optimization. We review some of the recent research in this direction that has been enabled with Ginkgo. We show how probabilistic programming can be used to efficiently sample the showering process, how a novel trellis algorithm can be used to efficiently marginalize over the enormous number of clustering histories for the same observed particles, and how dynamic programming, A* search, and reinforcement learning can be used to find the maximum likelihood clustering in this enormous search space. This work builds bridges with work in hierarchical clustering, statistics, combinatorial optmization, and reinforcement learning.
We study jet substructures of a boosted polarized top quark, which undergoes the semileptonic decay $tto bell u$, in the perturbative QCD framework. The jet mass distribution (energy profile) is factorized into the convolution of a hard top-quark decay kernel with the bottom-quark jet function (jet energy function). Computing the hard kernel to leading order in QCD and inputting the latter functions from the resummation formalism, we observe that the jet mass distribution is not sensitive to the helicity of the top quark, but the energy profile is: energy is accumulated faster within a left-handed top jet than within a right-handed one, a feature related to the $V-A$ structure of weak interaction. It is pointed out that the energy profile is a simple and useful jet observable for helicity discrimination of a boosted top quark, which helps identification of physics beyond the Standard Model at the Large Hadron Collider. The extension of our analysis to other jet substructures, including those associated with a hadronically decaying polarized top quark, is proposed.
We study jet substructures of a boosted polarized top quark, which undergoes the hadronic decay $tto b ubar d$, in the perturbative QCD framework, focusing on the energy profile and the differential energy profile. These substructures are factorized into the convolution of a hard top-quark decay kernel with a bottom-quark jet function and a $W$-boson jet function, where the latter is further factorized into the convolution of a hard $W$-boson decay kernel with two light-quark jet functions. Computing the hard kernels to leading order in QCD and including the resummation effect in the jet functions, we show that the differential jet energy profile is a useful observable for differentiating the helicity of a boosted hadronic top quark: a right-handed top jet exhibits quick descent of the differential energy profile with the inner test cone radius $r$, which is attributed to the $mbox{V-A}$ structure of weak interaction and the dead-cone effect associated with the $W$-boson jet. The above helicity differentiation may help to reveal the chiral structure of physics beyond the Standard Model at high energies.
In this report we review recent theoretical progress and the latest experimental results in jet substructure from the Tevatron and the LHC. We review the status of and outlook for calculation and simulation tools for studying jet substructure. Following up on the report of the Boost 2010 workshop, we present a new set of benchmark comparisons of substructure techniques, focusing on the set of variables and grooming methods that are collectively known as top taggers. To facilitate further exploration, we have attempted to collect, harmonise, and publish software implementations of these techniques.
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