相关论文: Neural Filters for Jet Analysis

Multi-particle correlation techniques are frequently used to study jet shapes and yields in hadronic and nuclear collisions. To date, a standard assumption applied in such analyses is that the observed correlations arise from either jets…

Defect detection is a basic and essential task in automatic parts production, especially for automotive engine precision parts. In this paper, we propose a new idea to construct a deep convolutional network combining related knowledge of…

I review the main experimental results on jet physics in high-energy nucleus-nucleus collisions as studied via inclusive leading hadron spectra and di-hadron correlations at high transverse momentum. In central Au+Au at RHIC (sqrt(s_NN) =…

Reliable analysis of intracellular dynamic processes in time-lapse fluorescence microscopy images requires complete and accurate tracking of all small particles in all time frames of the image sequences. A fundamental first step towards…

Jet quenching in high-energy heavy-ion collisions can be used to probe properties of hot and dense quark-gluon plasma. We provide a brief introduction to the concept and framework for the study of jet quenching. Different approaches and…

Next-generation particle accelerators demand advanced beam-diagnostic capabilities to ensure high performance, operational reliability, and sustainable machine operation. Increasing beam intensities and stored energies make the precise…

Hard-jet correlations probe parton energy loss and the microscopic structure of the quark-gluon plasma formed in ultra-relativistic heavy-ion collisions. The correlation of high-$p_\mathrm{T}$ jets with other jets, hadrons, or electroweak…

Recent progress in the perturbative analysis of hadronic jets, especially in the context of hadron colliders, is discussed. The characteristic feature of this work is the emergence of a level of precision in the study of the strong…

Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machine-learning (ML) techniques have been successfully applied to…

The production in high-energy hadron collisions of a pair of jets with large rapidity separation is studied in an improved BFKL formalism. By recasting the analytic solution of the BFKL equation as an explicit order-by-order sum over…

In very high energy collisions, many particles are produced and distributed in the available phase space volume in various ways. With advent of new accelerator facilities (especially, for nucleus-nucleus collisions), the problem of pattern…

The study of the substructure of collimated particles from quarks and gluons, or jets, has the promise to reveal the details how color charges interact with the QCD plasma medium created in colliders such as RHIC and the LHC. Traditional…

This paper introduces supervised learning techniques for real-time selection (triggering) of hadronically decaying tau leptons in proton-proton colliders. By implementing classic machine learning decision trees and advanced deep learning…

Studies of fully-reconstructed jets in heavy-ion collisions aim at extracting thermodynamical and transport properties of hot and dense QCD matter. Recently, a plethora of new jet substructure observables have been theoretically and…

In high-energy particle collisions, the reconstruction of secondary vertices from heavy-flavour hadron decays is crucial for identifying and studying jets initiated by $b$- or $c$-quarks. Traditional methods, while effective, require…

Modern neural networks have revolutionized the fields of computer vision (CV) and Natural Language Processing (NLP). They are widely used for solving complex CV tasks and NLP tasks such as image classification, image generation, and machine…

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of…

Underwater explosions produce complex fluid phenomena relevant to diverse applications including maritime engineering, medical therapeutics, and inertial confinement fusion. These systems exhibit multiphase flows, chemical kinetics, and…

Processing marine seismic data is computationally demanding and consists of multiple time-consuming steps. Neural network based processing can, in theory, significantly reduce processing time and has the potential to change the way seismic…

Deep convolutional neural networks (DCNN) have recently shown promising results in low-level computer vision problems such as optical flow and disparity estimation, but still, have much room to further improve their performance. In this…