Salvatore Rappoccio

We present the report of the hadronic working group of the BOOST2010 workshop held at the University of Oxford in June 2010. The first part contains a review of the potential of hadronic decays of highly boosted particles as an aid for discovery at the LHC and a discussion of the status of tools developed to meet the challenge of reconstructing and isolating these topologies. In the second part, we present new results comparing the performance of jet grooming techniques and top tagging algorithms on a common set of benchmark channels. We also study the sensitivity of jet substructure observables to the uncertainties in Monte Carlo predictions.

In this paper, 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, harmonize and publish software implementations of these techniques.

This report of the BOOST2012 workshop presents the results of four working groups that studied key aspects of jet substructure. We discuss the potential of first-principle QCD calculations to yield a precise description of the substructure of jets and study the accuracy of state-of-the-art Monte Carlo tools. Limitations of the experiments' ability to resolve substructure are evaluated, with a focus on the impact of additional (pile-up) proton proton collisions on jet substructure performance in future LHC operating scenarios. A final section summarizes the lessons learnt from jet substructure analyses in searches for new physics in the production of boosted top quarks.

Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those initiated by quarks, gluons, or by boosted heavy objects, such as top quarks and W bosons. This report, originating from and motivated by the BOOST2013 workshop, presents original particle-level studies that aim to improve our understanding of the relationships between jet substructure observables, their complementarity, and their dependence on the underlying jet properties, particularly the jet radius and jet transverse momentum. This is explored in the context of quark/gluon discrimination, boosted W boson tagging and boosted top quark tagging.

We explore the scale-dependence and correlations of jet substructure observables to improve upon existing techniques in the identification of highly Lorentz-boosted objects. Modified observables are designed to remove correlations from existing theoretically well-understood observables, providing practical advantages for experimental measurements and searches for new phenomena. We study such observables in $W$ jet tagging and provide recommendations for observables based on considerations beyond signal and background efficiencies.

The standard model of particle physics is an extremely successful theory of fundamental interactions, but it has many known limitations. It is therefore widely believed to be an effective field theory that describes interactions near the TeV scale. A plethora of strategies exist to extend the standard model, many of which contain predictions of new particles or dynamics that could manifest in proton-proton collisions at the Large Hadron Collider (LHC). As of now, none have been observed, and much of the available phase space for natural solutions to outstanding problems is excluded. If new physics exists, it is therefore either heavy (i.e. above the reach of current searches) or hidden (i.e. currently indistinguishable from standard model backgrounds). We summarize the existing searches, and discuss future directions at the LHC.

A bstract A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs (“eXpert AUGmented” variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.

Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy Frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy Frontier physics program.

Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize the internal structure of jets in order to distinguish those initiated by quarks, gluons, or by boosted heavy objects, such as top quarks and W bosons. This report, originating from and motivated by the BOOST2013 workshop, presents original particle-level studies that aim to improve our understanding of the relationships between jet substructure observables, their complementarity, and their dependence on the underlying jet properties, particularly the jet radius and jet transverse momentum. This is explored in the context of quark/gluon discrimination, boosted W boson tagging and boosted top quark tagging.

This report describes the studies performed for the Snowmass "Top algorithms and detectors" High Energy Frontier Study Group.

For The CMS CollaborationA new top-jet-tagging algorithm is presented based on work in Ref [1].The full results can be seen in Refs [2,3].This algorithm uses the Cambridge-Aachen jet clustering algorithm to decompose highly boosted top jets into subjet components and examine the kinematics of these subjets.With this algorithm, an efficiency of 46% for top-jets with p T = 600 GeV/c is obtained, together with a rejection of 98.5% for non-top jets with p T = 600 GeV/c.With 100 pb -1 of 10 TeV pp collider data collected at CMS, narrow tt resonances in the allhadronic channel, with masses of 1, 2, 3, and 4 TeV/c 2 can be excluded at the 95% C.L. for cross sections of 17.2, 1.5, 0.7, and 0.8 pb, respectively.A 5σ discovery can be made for cross sections of 43.6, 4.0, 1.6, and 1.3 pb, respectively.

The CMS Physics Analysis Toolkit (PAT) is presented. The PAT is a high-level analysis layer enabling the development of common analysis efforts across and within Physics Analysis Groups. It aims at fulfilling the needs of most CMS analyses, providing both ease-of-use for the beginner and flexibility for the advanced user. The main PAT concepts are described in detail and some examples from physics analyses are given.

Over the next ten years, the physics reach of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) will be greatly extended through increases in the instantaneous luminosity of the accelerator and large increases in the amount of collected data. Due to changes in the way Moore's Law computing performance gains have been realized in the past decade, an aggressive program of R&D is needed to ensure that the computing capability of CMS will be up to the task of collecting and analyzing this data.

A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs (eXpert AUGmented variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40\%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.

Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy frontier physics program.

An overview of searches for new physics in the ttbar sample from the CMS Collaboration is presented with data collected at the Large Hadron Collider at sqrt(s) = 7 TeV. There are several searches presented, including same-sign dilepton signatures, semileptonic signatures, and all-hadronic signatures, the latter of which uses advanced jet reconstruction techniques.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Salvatore Rappoccio, CMS Collaboration; Jets and jet substructure. AIP Conf. Proc. 28 September 2012; 1441 (1): 820–824. https://doi.org/10.1063/1.3700689 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

A new top-jet-tagging algorithm is presented based on work in Ref [1]. The full results can be seen in Refs [2, 3]. This algorithm uses the Cambridge-Aachen jet clustering algorithm to decompose highly boosted top jets into subjet components and examine the kinematics of these subjets. With this algorithm, an efficiency of 46% for top-jets with pT = 600 GeV/c is obtained, together with a rejection of 98.5% for non-top jets with pT = 600 GeV/c. With 100 pb−1 of 10 TeV pp collider data collected at CMS, narrow tt resonances in the allhadronic channel, with masses of 1, 2, 3, and 4 TeV/c2 can be excluded at the 95% C.L. for cross

We illustrate benefits to the U.S. economy and technological infrastructure of U.S. participation in accelerators overseas. We discuss contributions to experimental hardware and analysis and to accelerator technology and components, and benefits stemming from the involvement of U.S. students and postdoctoral fellows in global scientific collaborations. Contributed to the proceedings of the Snowmass 2013 Community Summer Study.

We illustrate benefits to the U.S. economy and technological infrastructure of U.S. participation in accelerators overseas. We discuss contributions to experimental hardware and analysis and to accelerator technology and components, and benefits stemming from the involvement of U.S. students and postdoctoral fellows in global scientific collaborations. Contributed to the proceedings of the Snowmass 2013 Community Summer Study.

We illustrate benefits to the U.S. economy and technological infrastructure of U.S. participation in accelerators overseas. We discuss contributions to experimental hardware and analysis and to accelerator technology and components, and benefits stemming from the involvement of U.S. students and postdoctoral fellows in global scientific collaborations. Contributed to the proceedings of the Snowmass 2013 Community Summer Study.

Over the next ten years, the physics reach of the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) will be greatly extended through increases in the instantaneous luminosity of the accelerator and large increases in the amount of collected data. Due to changes in the way Moore's Law computing performance gains have been realized in the past decade, an aggressive program of R&D is needed to ensure that the computing capability of CMS will be up to the task of collecting and analyzing this data.

Beyond the standard model theories like composite Higgs models predict resonances with large branching fractions in a Higgs boson and a vector boson with negligible branching fractions to light fermions. We present an overview of searches for new physics containing a Higgs boson and a W or Z boson in the final state, using proton-proton collision data collected with the CMS detector at the CERN LHC. For high-mass resonances decaying to intermediate bosons, the large boost for hadronic decays gives rise to one single "merged'' jet, which can be identified through a study of its substructure consistent with the presence of two quarks, enhancing the sensitivity due to the large branching ratios for hadronic decays. B-quark identification algorithms are used in addition to identify the hadronic H decays.

Abstract In high energy physics (HEP), analysis metadata comes in many forms—from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in an analysis, but designing analysis metadata systems has historically received little direct attention. Among other considerations, an ideal metadata tool should be easy to use by new analysers, should scale to large data volumes and diverse processing paradigms, and should enable future analysis reinterpretation. This document, which is the product of community discussions organised by the HEP Software Foundation, categorises types of metadata by scope and format and gives examples of current metadata solutions. Important design considerations for metadata systems, including sociological factors, analysis preservation efforts, and technical factors, are discussed. A list of best practices and technical requirements for future analysis metadata systems is presented. These best practices could guide the development of a future cross-experimental effort for analysis metadata tools.

A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs ("eXpert AUGmented" variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40\%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.

We present the measurement of the t$\bar{t}$ cross section in the lepton plus jets channel with ≥ 1 and ≥ 2 secondary vertex tags. We use the scalar sum of transverse energies of the event (HT) to discriminate t{bar t} from the other backgrounds. We also use the transverse mass of the leptonic W-boson (M$W\atop{T}$) to further reduce the Non-W backgrounds. We use a combination of data and Monte Carlo to estimate the backgrounds from electroweak processes, single top, fake leptons, W+ Light Flavor fake tags, and real W+ Heavy Flavor production. We obtain a value of σ ≥1 = 8.7$+0.9\atop{-0.9}$(stat)$+1.2\atop{-0.9}$(sys) pb for the ≥1 tag cross section, and σ ≥2 = 8.7$+1.8\atop{-1.6}$(stat)$+1.9\atop{-1.3}$(sys) pb for the ≥2 tag cross section. The authors also present a measurement of the t$\bar{t}$ cross section by fitting the Njet spectrum. They combine the =1 and ≥2 tag cross sections to obtain σt$\bar{t}$ = 8.9$+0.9\atop{-0.9}$(stat)$+1.4\atop{-1.3}$(syst)pb.

We present the measurement of the t{bar t} cross section in the lepton plus jets channel with {ge} 1 and {ge} 2 secondary vertex tags. We use the scalar sum of transverse energies of the event (H{sub T}) to discriminate t{bar t} from the other backgrounds. We also use the transverse mass of the leptonic W-boson (M{sub T}{sup W}) to further reduce the Non-W backgrounds. We use a combination of data and Monte Carlo to estimate the backgrounds from electroweak processes, single top, fake leptons, W+ Light Flavor fake tags, and real W+ Heavy Flavor production. We obtain a value of {sigma} {sub {ge}1} = 8.7{sub -0.9}{sup +0.9}(stat){sub -0.9}{sup +1.2}(sys) pb for the {ge}1 tag cross section, and {sigma}{sub {ge}2} = 8.7{sub -1.6}{sup +1.8}(stat){sub -1.3}{sup +1.9}(sys) pb for the {ge}2 tag cross section. The authors also present a measurement of the t{bar t} cross section by fitting the N{sub jet} spectrum. They combine the =1 and {ge}2 tag cross sections to obtain {sigma}{sub t{bar t}} = 8.9{sub -0.9}{sup +0.9}(stat){sub -1.3}{sup +1.4}(syst)pb.