Nothing Special   »   [go: up one dir, main page]

Ai et al., 2020 - Google Patents

A deep learning approach to multi-track location and orientation in gaseous drift chambers

Ai et al., 2020

View PDF
Document ID
4525090873778431519
Author
Ai P
Wang D
Sun X
Huang G
Li Z
Publication year
Publication venue
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

External Links

Snippet

Accurate measuring the location and orientation of individual particles in a beam monitoring system is of particular interest to researchers in multiple disciplines. Among feasible methods, gaseous drift chambers with hybrid pixel sensors have the great potential to …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/29—Measurement performed on radiation beams, e.g. position or section of the beam; Measurement of spatial distribution of radiation
    • G01T1/2914—Measurement of spatial distribution of radiation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/02—Dosimeters
    • G01T1/026—Semiconductor dose-rate meters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/16—Measuring radiation intensity
    • G01T1/17—Circuit arrangements not adapted to a particular type of detector
    • G01T1/178—Circuit arrangements not adapted to a particular type of detector for measuring specific activity in the presence of other radioactive substances, e.g. natural, in the air or in liquids such as rain water

Similar Documents

Publication Publication Date Title
Renner et al. Background rejection in NEXT using deep neural networks
Battat et al. Reducing DRIFT backgrounds with a submicron aluminized-mylar cathode
Huth A high rate testbeam data acquisition system and characterization of high voltage monolithic active pixel sensors
Qi et al. Radionuclide identification method for NaI low-count gamma-ray spectra using artificial neural network
Mendes et al. Radioactive hot-spot localisation and identification using deep learning
Ai et al. A deep learning approach to multi-track location and orientation in gaseous drift chambers
Schoorlemmer et al. Characteristics of extensive air showers around the energy threshold for ground-particle-based γ-ray observatories
Qi et al. Comparison of machine learning approaches for radioisotope identification using NaI (TI) gamma-ray spectrum
Reidt Studies for the ALICE inner tracking system upgrade
Tripathy et al. Material discrimination in cosmic muon imaging using Pattern Recognition Method
Kim et al. Identification of multiple radioisotopes through convolutional neural networks trained on 2-D transformed gamma spectral data from CsI (Tl) spectrometer
Wang et al. Explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping
Zhao et al. Low-resolution gamma-ray spectrum analysis using comprehensive training set and deep ResNet architecture
Giagu et al. Tau lepton identification with graph neural networks at future electron–positron colliders
Hoang A pattern recognition approach to learning tracks of heavy-ion particles in timepix detectors
Durbin et al. Development of a fully connected residual neural network for directional gamma ray detection
Genty The MicroBooNE search for anomalous electron neutrino appearance using image based data reconstruction
Leisos et al. Detection of high energy showers by the Astroneu extensive air shower array
Amoroso et al. PARSIFAL: a toolkit for triple-GEM parametrized simulation
Peralta et al. Particle classification in the LAGO water Cherenkov detectors using clustering algorithms
Kliemt Simulations with the PANDA micro-vertex-detector
Poonthottathil Machine learning in experimental neutrino physics
Lewis et al. Primary track recovery in high-definition gas time projection chambers
Verma et al. Shower Identification in Calorimeter using Deep Learning
LU Significant improvement of Quark/Gluon separation with the ATLAS detector at the LHC