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A novel deep neural network classifier, a ``Particle transformer'' (PaRT), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the…

高能物理 - 实验 · 物理学 2026-04-14 CMS Collaboration

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…

高能物理 - 唯象学 · 物理学 2015-07-21 Koichi Hamaguchi , Seng Pei Liew , Martin Stoll

Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging…

仪器与探测器 · 物理学 2025-11-14 CMS Collaboration

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…

高能物理 - 唯象学 · 物理学 2015-06-03 Tilman Plehn , Michael Spannowsky

Many analyses at the CERN LHC exploit the substructure of jets to identify heavy resonances produced with high momenta that decay into multiple quarks and/or gluons. This paper presents a new technique for correcting the substructure of…

高能物理 - 实验 · 物理学 2025-11-18 CMS Collaboration

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…

高能物理 - 唯象学 · 物理学 2016-08-31 Marat Freytsis , Tomer Volansky , Jonathan R. Walsh

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…

高能物理 - 唯象学 · 物理学 2008-11-26 David E. Kaplan , Keith Rehermann , Matthew D. Schwartz , Brock Tweedie

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…

高能物理 - 唯象学 · 物理学 2011-05-12 Yanou Cui , Zhenyu Han , Matthew D. Schwartz

In this paper we introduce a new approach to study jet substructure in the center-of-mass frame of the jet. We demonstrate that it can be used to discriminate the boosted heavy particles from the QCD jets and the method is complimentary to…

高能物理 - 唯象学 · 物理学 2015-03-19 Chunhui Chen

We discuss jet substructure in recombination algorithms for QCD jets and single jets from heavy particle decays. We demonstrate that the jet algorithm can introduce significant systematic effects into the substructure. By characterizing…

高能物理 - 唯象学 · 物理学 2014-11-20 Stephen D. Ellis , Christopher K. Vermilion , Jonathan R. Walsh

Observables which distinguish boosted topologies from QCD jets are playing an increasingly important role at the Large Hadron Collider (LHC). These observables are often used in conjunction with jet grooming algorithms, which reduce…

高能物理 - 唯象学 · 物理学 2018-04-04 Andrew J. Larkoski , Ian Moult , Duff Neill

The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstruct particles using the Particle Flow algorithm, connecting information from the tracker, muon, and calorimeter detectors, and enabling fine-grained reconstruction of high…

高能物理 - 实验 · 物理学 2023-10-13 Sioni Summers , Ioannis Bestintzanos , Giovanni Petrucciani

Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally intensive and require substantial data for training.…

高能物理 - 唯象学 · 物理学 2024-06-04 A. Hammad , Mihoko M. Nojiri

In this study, we introduce the More-Interaction Particle Transformer (MIParT), a novel deep learning neural network designed for jet tagging. This framework incorporates our own design, the More-Interaction Attention (MIA) mechanism, which…

高能物理 - 唯象学 · 物理学 2024-09-27 Yifan Wu , Kun Wang , Congqiao Li , Huilin Qu , Jingya Zhu

We present a new method to expose the dead cone effect at colliders using iterative declustering techniques. Iterative declustering allows to unwind the jet clustering and to access the subjets or branches at different depths of the jet…

高能物理 - 唯象学 · 物理学 2020-06-23 Leticia Cunqueiro , Mateusz Ploskon

The hard-scatter processes in hadronic collisions are often largely contaminated with soft background coming from pileup in proton-proton collisions, or underlying event in heavy-ion collisions. This paper presents a new background…

高能物理 - 唯象学 · 物理学 2024-11-28 Peter Berta , Lucia Masetti , David W. Miller , Martin Spousta

Being able to distinguish parton pair type in a dijet event could significantly improve the search for new particles that are predicted by the theories beyond the Standard Model at the Large Hadron Collider. To explore whether parton pair…

高能物理 - 唯象学 · 物理学 2014-10-31 Sertac Ozturk

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…

高能物理 - 唯象学 · 物理学 2020-07-15 J. A. Aguilar-Saavedra , B. Zaldivar

Jets can be used to probe the physical properties of the high energy density matter created in collisions at the Relativistic Heavy Ion Collider (RHIC). Measurements of strong suppression of inclusive hadron distributions and di-hadron…

核实验 · 物理学 2019-08-13 Sevil Salur

Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP). Nonetheless, most physics-inspired modern architectures are computationally inefficient and lack interpretability. This is especially…

计算物理 · 物理学 2023-01-31 Jose M Munoz , Ilyes Batatia , Christoph Ortner
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