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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

The main modification to high $p_t$ jets evolving in the quark gluon plasma is the depletion of their energy to the thermal background. Despite the prominent role played by energy loss effects in jet quenching, their theoretical description…

High Energy Physics - Phenomenology · Physics 2024-09-30 João Barata , Ian Moult , João M. Silva

Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at the CERN Large Hadron Collider. We explore the use of…

Data Analysis, Statistics and Probability · Physics 2021-11-29 Steven Tsan , Raghav Kansal , Anthony Aportela , Daniel Diaz , Javier Duarte , Sukanya Krishna , Farouk Mokhtar , Jean-Roch Vlimant , Maurizio Pierini

We present a survey of a comprehensive set of jet substructure observables commonly used to study the modifications of jets resulting from interactions with the Quark Gluon Plasma in Heavy Ion Collisions. The \jewel{} event generator is…

High Energy Physics - Phenomenology · Physics 2024-01-24 Miguel Crispim Romão , José Guilherme Milhano , Marco van Leeuwen

The STAR collaboration presents jet substructure measurements related to both the momentum fraction and the opening angle within jets in \pp and \AuAu collisions at \sqrtsn $= 200$ GeV. The substructure observables include SoftDrop groomed…

Nuclear Experiment · Physics 2022-05-04 STAR Collaboration , M. S. Abdallah , B. E. Aboona , J. Adam , L. Adamczyk , J. R. Adams , J. K. Adkins , G. Agakishiev , I. Aggarwal , M. M. Aggarwal , Z. Ahammed , I. Alekseev , D. M. Anderson , A. Aparin , E. C. Aschenauer , M. U. Ashraf , F. G. Atetalla , A. Attri , G. S. Averichev , V. Bairathi , W. Baker , J. G. Ball Cap , K. Barish , A. Behera , R. Bellwied , P. Bhagat , A. Bhasin , J. Bielcik , J. Bielcikova , I. G. Bordyuzhin , J. D. Brandenburg , A. V. Brandin , I. Bunzarov , X. Z. Cai , H. Caines , M. Calderón de la Barca Sánchez , D. Cebra , I. Chakaberia , P. Chaloupka , B. K. Chan , F-H. Chang , Z. Chang , N. Chankova-Bunzarova , A. Chatterjee , S. Chattopadhyay , D. Chen , J. Chen , J. H. Chen , X. Chen , Z. Chen , J. Cheng , M. Chevalier , S. Choudhury , W. Christie , X. Chu , H. J. Crawford , M. Csanád , M. Daugherity , T. G. Dedovich , I. M. Deppner , A. A. Derevschikov , A. Dhamija , L. Di Carlo , L. Didenko , P. Dixit , X. Dong , J. L. Drachenberg , E. Duckworth , J. C. Dunlop , N. Elsey , J. Engelage , G. Eppley , S. Esumi , O. Evdokimov , A. Ewigleben , O. Eyser , R. Fatemi , F. M. Fawzi , S. Fazio , P. Federic , J. Fedorisin , C. J. Feng , Y. Feng , P. Filip , E. Finch , Y. Fisyak , A. Francisco , C. Fu , L. Fulek , C. A. Gagliardi , T. Galatyuk , F. Geurts , N. Ghimire , A. Gibson , K. Gopal , X. Gou , D. Grosnick , A. Gupta , W. Guryn , A. I. Hamad , A. Hamed , Y. Han , S. Harabasz , M. D. Harasty , J. W. Harris , H. Harrison , S. He , W. He , X. H. He , Y. He , S. Heppelmann , S. Heppelmann , N. Herrmann , E. Hoffman , L. Holub , Y. Hu , H. Huang , H. Z. Huang , S. L. Huang , T. Huang , X. Huang , Y. Huang , T. J. Humanic , G. Igo , D. Isenhower , W. W. Jacobs , C. Jena , A. Jentsch , Y. Ji , J. Jia , K. Jiang , X. Ju , E. G. Judd , S. Kabana , M. L. Kabir , S. Kagamaster , D. Kalinkin , K. Kang , D. Kapukchyan , K. Kauder , H. W. Ke , D. Keane , A. Kechechyan , M. Kelsey , Y. V. Khyzhniak , D. P. Kikoła , C. Kim , B. Kimelman , D. Kincses , I. Kisel , A. Kiselev , A. G. Knospe , H. S. Ko , L. Kochenda , L. K. Kosarzewski , L. Kramarik , P. Kravtsov , L. Kumar , S. Kumar , R. Kunnawalkam Elayavalli , J. H. Kwasizur , R. Lacey , S. Lan , J. M. Landgraf , J. Lauret , A. Lebedev , R. Lednicky , J. H. Lee , Y. H. Leung , C. Li , C. Li , W. Li , X. Li , Y. Li , X. Liang , Y. Liang , R. Licenik , T. Lin , Y. Lin , M. A. Lisa , F. Liu , H. Liu , H. Liu , P. Liu , T. Liu , X. Liu , Y. Liu , Z. Liu , T. Ljubicic , W. J. Llope , R. S. Longacre , E. Loyd , N. S. Lukow , X. F. Luo , L. Ma , R. Ma , Y. G. Ma , N. Magdy , D. Mallick , S. Margetis , C. Markert , H. S. Matis , J. A. Mazer , N. G. Minaev , S. Mioduszewski , B. Mohanty , M. M. Mondal , I. Mooney , D. A. Morozov , A. Mukherjee , M. Nagy , J. D. Nam , Md. Nasim , K. Nayak , D. Neff , J. M. Nelson , D. B. Nemes , M. Nie , G. Nigmatkulov , T. Niida , R. Nishitani , L. V. Nogach , T. Nonaka , A. S. Nunes , G. Odyniec , A. Ogawa , S. Oh , V. A. Okorokov , B. S. Page , R. Pak , J. Pan , A. Pandav , A. K. Pandey , Y. Panebratsev , P. Parfenov , B. Pawlik , D. Pawlowska , C. Perkins , L. Pinsky , R. L. Pintér , J. Pluta , B. R. Pokhrel , G. Ponimatkin , J. Porter , M. Posik , V. Prozorova , N. K. Pruthi , M. Przybycien , J. Putschke , H. Qiu , A. Quintero , C. Racz , S. K. Radhakrishnan , N. Raha , R. L. Ray , R. Reed , H. G. Ritter , M. Robotkova , O. V. Rogachevskiy , J. L. Romero , D. Roy , L. Ruan , J. Rusnak , N. R. Sahoo , H. Sako , S. Salur , J. Sandweiss , S. Sato , W. B. Schmidke , N. Schmitz , B. R. Schweid , F. Seck , J. Seger , M. Sergeeva , R. Seto , P. Seyboth , N. Shah , E. Shahaliev , P. V. Shanmuganathan , M. Shao , T. Shao , A. I. Sheikh , D. Shen , S. S. Shi , Y. Shi , Q. Y. Shou , E. P. Sichtermann , R. Sikora , M. Simko , J. Singh , S. Singha , M. J. Skoby , N. Smirnov , Y. Söhngen , W. Solyst , P. Sorensen , H. M. Spinka , B. Srivastava , T. D. S. Stanislaus , M. Stefaniak , D. J. Stewart , M. Strikhanov , B. Stringfellow , A. A. P. Suaide , M. Sumbera , B. Summa , X. M. Sun , X. Sun , Y. Sun , Y. Sun , B. Surrow , D. N. Svirida , Z. W. Sweger , P. Szymanski , A. H. Tang , Z. Tang , A. Taranenko , T. Tarnowsky , J. H. Thomas , A. R. Timmins , D. Tlusty , T. Todoroki , M. Tokarev , C. A. Tomkiel , S. Trentalange , R. E. Tribble , P. Tribedy , S. K. Tripathy , T. Truhlar , B. A. Trzeciak , O. D. Tsai , Z. Tu , T. Ullrich , D. G. Underwood , I. Upsal , G. Van Buren , J. Vanek , A. N. Vasiliev , I. Vassiliev , V. Verkest , F. Videbæk , S. Vokal , S. A. Voloshin , F. Wang , G. Wang , J. S. Wang , P. Wang , Y. Wang , Y. Wang , Z. Wang , J. C. Webb , P. C. Weidenkaff , L. Wen , G. D. Westfall , H. Wieman , S. W. Wissink , J. Wu , J. Wu , Y. Wu , B. Xi , Z. G. Xiao , G. Xie , W. Xie , H. Xu , N. Xu , Q. H. Xu , Y. Xu , Z. Xu , Z. Xu , C. Yang , Q. Yang , S. Yang , Y. Yang , Z. Ye , Z. Ye , L. Yi , K. Yip , Y. Yu , H. Zbroszczyk , W. Zha , C. Zhang , D. Zhang , J. Zhang , S. Zhang , S. Zhang , X. P. Zhang , Y. Zhang , Y. Zhang , Y. Zhang , Z. J. Zhang , Z. Zhang , Z. Zhang , J. Zhao , C. Zhou , X. Zhu , M. Zurek , M. Zyzak

In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to…

High Energy Physics - Phenomenology · Physics 2015-12-18 Vein S Kong , Jiakun Li , Yujia Zhang

At the LHC, using forward + central detectors, it becomes possible for the first time to carry out measurements of the transverse energy flow due to ``minijets" accompanying production of two jets separated by a large rapidity interval. We…

High Energy Physics - Phenomenology · Physics 2011-12-30 M. Deak , F. Hautmann , H. Jung , K. Kutak

Jet substructure observables play a central role at the Large Hadron Collider for identifying the boosted hadronic decay products of electroweak scale resonances. The complete description of these observables requires understanding both the…

High Energy Physics - Phenomenology · Physics 2016-01-27 Andrew J. Larkoski , Ian Moult

Jets are an important probe to identify the hard interaction of interest at the LHC. They are routinely used in Standard Model precision measurements as well as in searches for new heavy particles, including jet substructure methods. In…

High Energy Physics - Phenomenology · Physics 2016-08-24 Piotr Pietrulewicz , Frank J. Tackmann , Wouter J. Waalewijn

Machine learning methods have shown great success in various scientific areas, including fluid mechanics. However, reconstruction problems, where full velocity fields must be recovered from partial observations, remain challenging. In this…

Fluid Dynamics · Physics 2025-01-16 Qian Zhang , Dmitry Krotov , George Em Karniadakis

In this paper, we explore the use of jet substructure as a way of probing phenomena which break the isotropic behavior of jets, such as jet propagation through an anisotropically flowing quark-gluon plasma or spin correlations. We introduce…

High Energy Physics - Phenomenology · Physics 2024-12-18 Weiyao Ke , John Terry , Ivan Vitev

Modern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W…

High Energy Physics - Phenomenology · Physics 2018-10-17 Katherine Fraser , Matthew D. Schwartz

Supermassive black holes can launch powerful jets which can be some of the most luminous multi-wavelength sources; decades after their discovery their physics and energetics are still poorly understood. The past decade has seen a dramatic…

High Energy Astrophysical Phenomena · Physics 2018-11-07 M. Lucchini , S. Markoff , P. Crumley , F. Krauß , R. M. T. Connors

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the…

Data Analysis, Statistics and Probability · Physics 2021-02-10 Francesco Armando Di Bello , Sanmay Ganguly , Eilam Gross , Marumi Kado , Michael Pitt , Lorenzo Santi , Jonathan Shlomi

We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery…

High Energy Physics - Phenomenology · Physics 2023-07-19 Matthew Leigh , John Andrew Raine , Knut Zoch , Tobias Golling

Jet substructure is playing a central role at the Large Hadron Collider (LHC) probing the Standard Model in extreme regions of phase space and providing innovative ways to search for new physics. Analytic calculations of experimentally…

High Energy Physics - Phenomenology · Physics 2017-08-24 Andrew J. Larkoski , Ian Moult , Duff Neill

Jet substructure observables serve as essential tools for probing the quark-gluon plasma produced in relativistic heavy-ion collisions. Their interpretation, however, is often complicated by edge effects, which arise when correlated…

High Energy Physics - Phenomenology · Physics 2025-12-12 Carlota Andres , Jack Holguin , Benjamin Kimelman , Raghav Kunnawalkam Elayavalli , Jussi Viinikainen , Zhong Yang

Jets with transverse energy of few TeV are becoming now common in LHC data. Most of these jets are produced by QCD processes and some from the collimated decay of highly boosted objects like W, Z, H0 and top-quark. The study of such QCD…

High Energy Physics - Phenomenology · Physics 2013-05-22 Ehud Duchovni

Zonal jets are striking and beautiful examples of the propensity for geophysical turbulent flows to spontaneously self-organize into robust, large scale coherent structures. There exist many dynamical mechanisms for the formation of zonal…

Fluid Dynamics · Physics 2016-02-24 F Bouchet , Antoine Venaille

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…

High Energy Physics - Phenomenology · Physics 2015-05-27 Anson Hook , Martin Jankowiak , Jay G. Wacker