scholarly journals CNN-based event classification of alpha-decay events in nuclear emulsion

2021 ◽  
Vol 989 ◽  
pp. 164930
Author(s):  
J. Yoshida ◽  
H. Ekawa ◽  
A. Kasagi ◽  
M. Nakagawa ◽  
K. Nakazawa ◽  
...  
Keyword(s):  
Nuclear Emulsion ◽  
Alpha Decay ◽  
Event Classification

scholarly journals End-to-End Event Classification of High-Energy Physics Data

2018 ◽  
Vol 1085 ◽  
pp. 042022 ◽  
Author(s):  
M Andrews ◽  
M Paulini ◽  
S Gleyzer ◽  
B Poczos
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Event Classification ◽  
End To End ◽  
Energy Physics

scholarly journals Analysis of a Single-Λ Hypernuclear Event in KEK-PS E373 Nuclear Emulsion Experiment

2019 ◽  
Vol 206 ◽  
pp. 09003
Author(s):  
Myo Thandar Aung ◽  
Thida Wint ◽  
Khin Swe Myint ◽  
Kazuma Nakazawa
Keyword(s):  
Nuclear Emulsion ◽  
Alpha Decay ◽  
Energy Relation ◽  
Nucleon Interaction ◽  
Decay Modes ◽  
Mesonic Decay ◽  
Decay Products ◽  
Thorium Series ◽  
Events Data ◽  
Mass Reconstruction

The purpose of this research is to identify a single-Λ hypernucleus and its decay products which support to get more information about hyperon-nucleon interaction. Before performing the analysis of a single-Λ hypernucleus, first, we have deduced the density of emulsion by calibrating the range-energy relation using alpha decay events data from thorium series. It is very important for the mass reconstruction of hypernucleus events in nuclear emulsion. And we have reconstructed a single-Λ hypernucleus event by using the kinematical reconstruction for mesonic and non-mesonic decay modes. From the result of our analysis, we can uniquely identify that single-Λ hypernucleus is $ {}_\Lambda ^9 B $ and decay products are 4He,3He, proton and neutron.

scholarly journals Power quality event classification using complex wavelets phasor models and customized convolution neural network

2022 ◽  
Vol 12 (1) ◽  
pp. 22
Author(s):  
Likhitha Ramalingappa ◽  
Aswathnarayan Manjunatha
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Wavelet Transforms ◽  
Input Voltage ◽  
Convolution Neural Network ◽  
Training Time ◽  
Event Classification ◽  
Low Pass ◽  
Space Phasor

Origin and triggers of power quality (PQ) events must be identified in prior, in order to take preventive steps to enhance power quality. However it is important to identify, localize and classify the PQ events to determine the causes and origins of PQ disturbances. In this paper a novel algorithm is presented to classify voltage variations into six different PQ events considering the space phasor model (SPM) diagrams, dual tree complex wavelet transforms (DTCWT) sub bands and the convolution neural network (CNN) model. The input voltage data is converted into SPM data, the SPM data is transformed using 2D DTCWT into low pass and high pass sub bands which are simultaneously processed by the 2D CNN model to perform classification of PQ events. In the proposed method CNN model based on Google Net is trained to perform classification of PQ events with default configuration as in deep neural network designer in MATLAB environment. The proposed algorithm achieve higher accuracy with reduced training time in classification of events than compared with reported PQ event classification methods.

Validity of the latest interpretation on primary energy estimation and dependence of on laboratory momentum

1979 ◽  
Vol 57 (2) ◽  
pp. 182-185
Author(s):  
B. K. Bandyopadhyay ◽  
B. K. Betal
Keyword(s):  
Experimental Data ◽  
Proton Beam ◽  
Proton Energy ◽  
Nuclear Emulsion ◽  
Primary Energy ◽  
New Method ◽  
Incident Proton ◽  
Energy Estimation ◽  
Laboratory Momentum

Interpretation on primary energy estimation by Bhowmik, Singh, and Kaul has been substantiated from the data obtained from interactions of a 70 GeV proton beam in nuclear emulsion. Criteria A and B have been applied for the classification of the cascade mechanism and tube mechanism for the energy estimation of individual events. From our experimental data it has been shown that the percentage of coherent production is not as high as claimed by this group, but energy estimation by this new method agrees fairly well with our incident proton energy of E = 70 GeV. Moreover, it is found that [Formula: see text] at 70 GeV/c and our data indicate that[Formula: see text] is independent of laboratory momentum beyond 20 GeV/c.

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