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Jets are algorithmic proxies of hard scattered partons, i.e. quarks and gluons, in high energy particle collisions. The STAR collaboration presents the first measurements of substructure observables at the first, second and third splits in…

High Energy Physics - Experiment · Physics 2021-08-02 Raghav Kunnawalkam Elayavalli

The two-prong structure related to the leading subjets inside a reconstructed jet opens new avenues toward precision constraints on the in-medium modification of parton showers. In this talk, we present the first resummed calculation of the…

High Energy Physics - Phenomenology · Physics 2018-12-11 Hai Tao Li , Ivan Vitev

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.…

High Energy Physics - Phenomenology · Physics 2024-06-04 A. Hammad , Mihoko M. Nojiri

Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional…

High Energy Physics - Phenomenology · Physics 2022-02-22 Soham Bhattacharya , Monoranjan Guchait , Aravind H. Vijay

We present a generic method for improving the effectiveness of heavy particle searches in hadronic channels at the Large Hadron Collider. By selectively removing, or pruning, protojets from the substructure provided by a k_T-style jet…

High Energy Physics - Phenomenology · Physics 2009-11-06 Stephen D. Ellis , Christopher K. Vermilion , Jonathan R. Walsh

We propose a novel model-independent method to search for W' bosons at the Large Hadron Collider by looking at dijets where one jet is identified as a boosted-top jet and another jet is tagged as a boosted-bottom jet. Performing a detector…

High Energy Physics - Phenomenology · Physics 2014-07-30 Daniel Duffty , Zack Sullivan

Deep neural networks trained for jet tagging are typically specific to a narrow range of transverse momenta or jet masses. Given the large phase space that the LHC is able to probe, the potential benefit of classifiers that are effective…

High Energy Physics - Phenomenology · Physics 2022-06-03 Matthew J. Dolan , Ayodele Ore

In the first talk I discuss the usefulness of jet grooming for testing jet quenching mechanisms, and I present a calculation of soft-drop jet mass distribution in proton-proton and heavy ion collisions. In the second talk I discuss the…

High Energy Physics - Phenomenology · Physics 2019-01-25 Yang-Ting Chien

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

Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that…

Computer Vision and Pattern Recognition · Computer Science 2015-04-23 Qingshan Liu , Jing Yang , Kaihua Zhang , Yi Wu

Jets in relativistic heavy-ion collisions (HICs) interact with the quark-gluon plasma (QGP), leading to effects such as a suppression of jet yields and a modification of internal jet structure that are used to measure the properties of the…

Nuclear Experiment · Physics 2022-09-01 Hannah Bossi

Jets, as collections of multi-scale objects, allow for insight into perturbative (high-momentum) processes, but gaining an understanding of the non-perturbative structure within jets such as hadronization effects and the underlying event…

High Energy Physics - Phenomenology · Physics 2024-02-19 Isaac Mooney

In this letter, measurements of the shared momentum fraction ($z_{\rm{g}}$) and the groomed jet radius ($R_{\rm{g}}$), as defined in the SoftDrop algorihm, are reported in \pp collisions at $\sqrt{s} = 200$ GeV collected by the STAR…

High Energy Physics - Experiment · Physics 2021-01-07 STAR Collaboration , J. Adam , L. Adamczyk , J. R. Adams , J. K. Adkins , G. Agakishiev , 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 , K. Barish , A. Behera , R. Bellwied , A. Bhasin , J. Bielcik , J. Bielcikova , L. C. Bland , I. G. Bordyuzhin , J. D. Brandenburg , A. V. Brandin , J. Butterworth , 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 , D. Chen , J. H. Chen , X. Chen , Z. Chen , J. Cheng , M. Cherney , M. Chevalier , S. Choudhury , W. Christie , H. J. Crawford , M. Csanád , M. Daugherity , T. G. Dedovich , I. M. Deppner , A. A. Derevschikov , L. Didenko , X. Dong , J. L. Drachenberg , J. C. Dunlop , T. Edmonds , N. Elsey , J. Engelage , G. Eppley , R. Esha , S. Esumi , O. Evdokimov , A. Ewigleben , O. Eyser , R. Fatemi , S. Fazio , P. Federic , J. Fedorisin , C. J. Feng , Y. Feng , P. Filip , E. Finch , Y. Fisyak , A. Francisco , L. Fulek , C. A. Gagliardi , T. Galatyuk , F. Geurts , A. Gibson , K. Gopal , D. Grosnick , W. Guryn , A. I. Hamad , A. Hamed , J. W. Harris , S. He , W. He , X. He , S. Heppelmann , S. Heppelmann , N. Herrmann , E. Hoffman , L. Holub , Y. Hong , S. Horvat , Y. Hu , H. Z. Huang , S. L. Huang , T. Huang , X. Huang , T. J. Humanic , P. Huo , G. Igo , D. Isenhower , W. W. Jacobs , C. Jena , A. Jentsch , Y. JI , J. Jia , K. Jiang , S. Jowzaee , 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 , T. A. Kinghorn , I. Kisel , A. Kiselev , A. Kisiel , M. Kocan , L. Kochenda , L. K. Kosarzewski , L. Kramarik , P. Kravtsov , K. Krueger , N. Kulathunga Mudiyanselage , L. 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 , W. Li , W. Li , X. Li , Y. Li , Y. Liang , R. Licenik , T. Lin , Y. Lin , M. A. Lisa , F. Liu , H. Liu , P. Liu , P. Liu , T. Liu , X. Liu , Y. Liu , Z. Liu , T. Ljubicic , W. J. Llope , R. S. Longacre , N. S. Lukow , S. Luo , X. Luo , G. L. Ma , L. Ma , R. Ma , Y. G. Ma , N. Magdy , R. Majka , D. Mallick , S. Margetis , C. Markert , H. S. Matis , J. A. Mazer , N. G. Minaev , S. Mioduszewski , B. Mohanty , M. M. Mondal , I. Mooney , Z. Moravcova , D. A. Morozov , M. Nagy , J. D. Nam , Md. Nasim , K. Nayak , D. Neff , J. M. Nelson , D. B. Nemes , M. Nie , G. Nigmatkulov , T. Niida , L. V. Nogach , T. Nonaka , G. Odyniec , A. Ogawa , S. Oh , V. A. Okorokov , B. S. Page , R. Pak , A. Pandav , Y. Panebratsev , B. Pawlik , D. Pawlowska , H. Pei , C. Perkins , L. Pinsky , R. L. Pintér , J. Pluta , J. Porter , M. Posik , N. K. Pruthi , M. Przybycien , J. Putschke , H. Qiu , A. Quintero , S. K. Radhakrishnan , S. Ramachandran , R. L. Ray , R. Reed , H. G. Ritter , J. B. Roberts , O. V. Rogachevskiy , J. L. Romero , 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 , F. Shen , W. Q. Shen , S. S. Shi , Q. Y. Shou , E. P. Sichtermann , R. Sikora , M. Simko , J. Singh , S. Singha , N. Smirnov , 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 , Y. Sun , Y. Sun , B. Surrow , D. N. Svirida , P. Szymanski , A. H. Tang , Z. Tang , A. Taranenko , T. Tarnowsky , J. H. Thomas , A. R. Timmins , D. Tlusty , M. Tokarev , C. A. Tomkiel , S. Trentalange , R. E. Tribble , P. Tribedy , S. K. Tripathy , O. D. Tsai , Z. Tu , T. Ullrich , D. G. Underwood , I. Upsal , G. Van Buren , J. Vanek , A. N. Vasiliev , I. Vassiliev , 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 , R. Witt , Y. Wu , Z. G. Xiao , G. Xie , W. Xie , H. Xu , N. Xu , Q. H. Xu , Y. F. Xu , Y. Xu , Z. Xu , Z. Xu , C. Yang , Q. Yang , S. Yang , Y. Yang , Z. Yang , Z. Ye , Z. Ye , L. Yi , K. Yip , H. Zbroszczyk , W. Zha , D. Zhang , S. Zhang , S. Zhang , X. P. Zhang , Y. Zhang , Y. Zhang , Z. J. Zhang , Z. Zhang , J. Zhao , C. Zhong , C. Zhou , X. Zhu , Z. Zhu , M. Zurek , M. Zyzak

The MUST (Mass Unspecific Supervised Tagging) method has proven to be successful in implementing generic jet taggers capable of discriminating various signals over a wide range of jet masses. We implement the MUST concept by using eXtreme…

High Energy Physics - Phenomenology · Physics 2024-11-26 J. A. Aguilar-Saavedra , E. Arganda , F. R. Joaquim , R. M. Sandá Seoane , J. F. Seabra

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…

High Energy Physics - Phenomenology · Physics 2015-03-19 Chunhui Chen

Multi-object tracking (MOT) is an essential task in the computer vision field. With the fast development of deep learning technology in recent years, MOT has achieved great improvement. However, some challenges still remain, such as…

Computer Vision and Pattern Recognition · Computer Science 2021-05-07 Gaoang Wang , Yizhou Wang , Renshu Gu , Weijie Hu , Jenq-Neng Hwang

We present a new technique for tagging heavy-flavor jets with p_T > 500 GeV called "mu_x tagging." Current track-based methods of b-jet tagging lose efficiency and experience a large rise in fake rate in the boosted regime. Using muons from…

High Energy Physics - Phenomenology · Physics 2016-02-17 Keith Pedersen , Zack Sullivan

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…

High Energy Physics - Phenomenology · Physics 2015-06-03 Tilman Plehn , Michael Spannowsky

We introduce a new jet substructure technique called "soft drop declustering", which recursively removes soft wide-angle radiation from a jet. The soft drop algorithm depends on two parameters--a soft threshold $z_\text{cut}$ and an angular…

High Energy Physics - Phenomenology · Physics 2014-06-03 Andrew J. Larkoski , Simone Marzani , Gregory Soyez , Jesse Thaler

Jet substructure techniques are playing an essential role in exploring the TeV scale at the Large Hadron Collider (LHC), since they facilitate the efficient reconstruction and identification of highly-boosted objects. Both for the LHC and…

High Energy Physics - Phenomenology · Physics 2018-01-11 Evan Coleman , Marat Freytsis , Andreas Hinzmann , Meenakshi Narain , Jesse Thaler , Nhan Tran , Caterina Vernieri
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