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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…

High Energy Physics - Experiment · Physics 2017-08-10 Jannicke Pearkes , Wojciech Fedorko , Alison Lister , Colin Gay

In the top quark pair production in association with the Higgs boson decaying to a b quark pair t-tbar H (b-bbar), the final state has an irreducible nonresonant background from the production of a top quark pair in association with a b…

High Energy Physics - Experiment · Physics 2020-12-30 Jieun Choi , Tae Jeong Kim , Jongwon Lim , Jiwon Park , Yeonsu Ryou , Juhee Song , Soohyun Yun

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…

High Energy Physics - Phenomenology · Physics 2011-05-12 Yanou Cui , Zhenyu Han , Matthew D. Schwartz

Deep learning techniques have the power to identify the degree of modification of high energy jets traversing deconfined QCD matter on a jet-by-jet basis. Such knowledge allows us to study jets based on their initial, rather than final…

High Energy Physics - Phenomenology · Physics 2022-04-04 Yi-Lun Du , Daniel Pablos , Konrad Tywoniuk

Di-jet event rates have been measured for deep-inelastic scattering in the kinematic domain ~5 < Q^2 < ~100 GeV^2 and ~10^(-4) < x_Bj < ~10^(-2), and for jet transverse momenta squared p_t^2 > ~Q^2. The analysis is based on data collected…

High Energy Physics - Experiment · Physics 2008-11-26 C. Adloff

We apply both cut-based and machine learning techniques using the same inputs to the challenge of hadronic jet substructure recognition, utilizing classical subjettiness variables within the Delphes parameterized detector simulation…

High Energy Physics - Phenomenology · Physics 2024-10-21 Jiří Kvita , Petr Baroň , Monika Machalová , Radek Přívara , Rostislav Vodák , Jan Tomeček

We apply gradient boosting machine learning techniques to the problem of hadronic jet substructure recognition using classical subjettiness variables available within a common parameterized detector simulation package DELPHES. Per-jet…

High Energy Physics - Experiment · Physics 2024-01-25 Petr Baroň , Jiří Kvita , Radek Přívara , Jan Tomeček , Rostislav Vodák

A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which…

High Energy Physics - Experiment · Physics 2020-10-16 CMS Collaboration

A deep-learning approach based on the transformer architecture is developed to distinguish between jets originating from quarks and gluons. The algorithm operates on jets with transverse momentum $p_{\text{T}} > 20$ and pseudorapidity…

High Energy Physics - Experiment · Physics 2025-12-04 ATLAS Collaboration

This paper presents studies of the performance of several jet-substructure techniques, which are used to identify hadronically decaying top quarks with high transverse momentum contained in large-radius jets. The efficiency of identifying…

High Energy Physics - Experiment · Physics 2016-07-05 ATLAS Collaboration

Jet flavour tagging is crucial in experimental high-energy physics. A tagging algorithm, DeepJetTransformer, is presented, which exploits a transformer-based neural network that is substantially faster to train than state-of-the-art graph…

High Energy Physics - Experiment · Physics 2025-02-11 Freya Blekman , Florencia Canelli , Alexandre De Moor , Kunal Gautam , Armin Ilg , Anna Macchiolo , Eduardo Ploerer

A search optimized for new heavy particles decaying to two $b$-quarks and produced in association with additional $b$-quarks is reported. The sensitivity is improved by $b$-tagging at least one lower-$p_{\rm{T}}$ jet in addition to the two…

High Energy Physics - Experiment · Physics 2024-01-22 ATLAS Collaboration

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and…

Instrumentation and Detectors · Physics 2018-05-10 CMS Collaboration

In this article, we investigate the possibility of enhancing the di-jet resonance searches by tagging the final state radiation (FSR) jet, using an event-level deep neural network. It is found that solely relying on the 4-momenta of the…

High Energy Physics - Phenomenology · Physics 2026-04-23 Bingxuan Liu , Yuxuan Shen , Yuanshunzi Sui

The $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process is an essential channel to reveal the Higgs properties but has an irreducible background from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, which produces a…

High Energy Physics - Experiment · Physics 2021-03-17 Cheongjae Jang , Sang-Kyun Ko , Yung-Kyun Noh , Jieun Choi , Jongwon Lim , Tae Jeong Kim

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…

High Energy Physics - Experiment · Physics 2026-04-14 CMS Collaboration

A search is performed for localized excesses in the low-mass dijet invariant mass distribution, targeting a hypothetical new particle decaying into two jets and produced in association with either a high transverse momentum photon or a jet.…

High Energy Physics - Experiment · Physics 2024-08-20 ATLAS Collaboration

A search is performed for narrow resonances decaying to final states of two jets, with at least one jet originating from a b quark, in proton-proton collisions at $\sqrt{s}$ = 13 TeV. The data set corresponds to an integrated luminosity of…

High Energy Physics - Experiment · Physics 2023-07-17 CMS Collaboration

A search is performed for localised excesses in dijet mass distributions of low-dijet-mass events produced in association with a high transverse energy photon. The search uses up to 79.8 fb$^{-1}$ of LHC proton-proton collisions collected…

High Energy Physics - Experiment · Physics 2019-06-14 ATLAS Collaboration

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to…

High Energy Physics - Phenomenology · Physics 2024-07-30 Amon Furuichi , Sung Hak Lim , Mihoko M. Nojiri
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