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Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use…

高能物理 - 唯象学 · 物理学 2016-09-06 Lester Mackey , Benjamin Nachman , Ariel Schwartzman , Conrad Stansbury

Jet finding is a type of optimization problem, where hadrons from a high-energy collision event are grouped into jets based on a clustering criterion. As three interesting examples, one can form a jet cluster that (1) optimizes the overall…

高能物理 - 唯象学 · 物理学 2015-10-08 Jesse Thaler

As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data…

计算机视觉与模式识别 · 计算机科学 2020-04-28 Luhong Diao , Jinying Gao1 , Manman Deng

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets…

We provide a general and malleable heuristic for the air conflict resolution problem. This heuristic is based on a new neighborhood structure for searching the solution space of trajectories and flight-levels. Using unsupervised learning,…

机器人学 · 计算机科学 2025-01-09 Mirmojtaba Gharibi , John-Paul Clarke

Recent developments in jet clustering are reviewed. We present a list of fast and infrared and collinear safe algorithms, and also describe new tools like jet areas. We show how these techniques can be applied to the study of underlying…

高能物理 - 唯象学 · 物理学 2009-06-10 Matteo Cacciari

We study the possibility to employ neural networks to simulate jet clustering procedures in high energy hadron-hadron collisions. We concentrate our analysis on the Fermilab Tevatron energy and on the $k_\bot$ algorithm. We consider both…

高能物理 - 唯象学 · 物理学 2016-09-01 P. De Felice , G. Nardulli , G. Pasquariello

Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario,…

机器学习 · 计算机科学 2019-09-13 Tim Jaschek , Marko Bucyk , Jaspreet S. Oberoi

A new algorithm for the identification of boosted, hadronically decaying, heavy particles at the LHC is presented. The algorithm is based on the known procedure of jet clustering with variable distance parameter $R$ and adapts the jet size…

高能物理 - 唯象学 · 物理学 2016-11-10 Tobias Lapsien , Roman Kogler , Johannes Haller

The event-by-event analysis of multiparticle production in high energy hadron and nuclei collisions can be performed using the discrete wavelet transformation. The ring-like and jet-like structures in two-dimensional angular histograms are…

核实验 · 物理学 2011-05-05 I. M. Dremin , G. Kh. Eyyubova , V. L. Korotkikh , L. I. Sarycheva

Clustering algorithms build jets though the iterative application of single particle and pairwise metrics. This leads to phase space constraints that are extremely complicated beyond the lowest orders in perturbation theory, and in practice…

高能物理 - 唯象学 · 物理学 2012-04-05 Randall Kelley , Jonathan R. Walsh , Saba Zuberi

High-multiplicity all-hadronic final states are an important, but difficult final state for searching for physics beyond the Standard Model. A powerful search method is to look for large jets with accidental substructure due to multiple…

高能物理 - 唯象学 · 物理学 2019-06-26 Joshua Lin , Wahid Bhimji , Benjamin Nachman

The size of non-perturbative corrections to high E_T jet production in deep-inelastic scattering is reviewed. Based on predictions from fragmentation models, hadronization corrections for different jet definitions are compared and the model…

高能物理 - 唯象学 · 物理学 2007-05-23 M. Wobisch , T. Wengler

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…

高能物理 - 唯象学 · 物理学 2022-04-04 Yi-Lun Du , Daniel Pablos , Konrad Tywoniuk

Cluster analysis relates to the task of assigning objects into groups which ideally present some desirable characteristics. When a cluster structure is confined to a subset of the feature space, traditional clustering techniques face…

机器学习 · 统计学 2026-04-14 Efthymios Costa , Ioanna Papatsouma , Angelos Markos

Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and…

数据分析、统计与概率 · 物理学 2025-08-15 Juvenal Bassa , Vidya Manian , Sudhir Malik , Arghya Chattopadhyay

In relativistic heavy-ion collisions, where quark-gluon plasma forms, hadron production is anisotropic at both low and high transverse momentum, driven by flow dynamics and spatial anisotropies. To better understand these mechanisms, we use…

核理论 · 物理学 2026-01-16 Tanner Mengel , Niseem Magdy , Ron Belmont , Anthony Timmins , Christine Nattrass

Jet finding algorithms, as they are used in $e^+ e^-$ and hadron collisions, are reviewed and compared. It is suggested that a successive combination style algorithm, similar to that used in $e^+ e^-$ physics, might be useful also in hadron…

高能物理 - 唯象学 · 物理学 2010-02-18 Stephen D. Ellis , Davision E. Soper

We investigate single-inclusive high-pT jet production in longitudinally polarized pp collisions at RHIC, with particular focus on the algorithm adopted to define the jets. Following and extending earlier work in the literature, we treat…

高能物理 - 唯象学 · 物理学 2015-06-11 Asmita Mukherjee , Werner Vogelsang

I propose a "quantum annealing" heuristic for the problem of combinatorial search among a frustrated set of states characterized by a cost function to be minimized. The algorithm is probabilistic, with postselection of the measurement…

量子物理 · 物理学 2009-11-07 Carlo A. Trugenberger