scholarly journals Investigation of the lightest hybrid meson candidate with a coupled-channel analysis of $${{\bar{p}}p}$$-, $$\pi ^- p$$- and $${\pi \pi }$$-Data

2021 ◽  
Vol 81 (12) ◽  
Author(s):  
B. Kopf ◽  
M. Albrecht ◽  
H. Koch ◽  
M. Küßner ◽  
J. Pychy ◽  
...  

AbstractA sophisticated coupled-channel analysis is presented that combines different processes: the channels $${\pi ^0\pi ^0\eta }$$ π 0 π 0 η , $${\pi ^0\eta \eta }$$ π 0 η η and $${K^+K^-\pi ^0}$$ K + K - π 0 from $${{\bar{p}}p}$$ p ¯ p annihilations, the P- and D-wave amplitudes of the $$\pi \eta $$ π η and $$\pi \eta ^\prime $$ π η ′ systems produced in $$\pi ^-p$$ π - p scattering, and data from $${\pi \pi }$$ π π -scattering reactions. Hence our analysis combines the data sets used in two independent previous analyses published by the Crystal Barrel experiment and by the JPAC group. Based on the new insights from these studies, this paper aims at a better understanding of the spin-exotic $$\pi _1$$ π 1 resonances in the light-meson sector. By utilizing the K-matrix approach and realizing the analyticity via Chew-Mandelstam functions the amplitude of the spin-exotic wave can be well described by a single $$\pi _1$$ π 1 pole for both systems, $$\pi \eta $$ π η and $$\pi \eta ^\prime $$ π η ′ . The mass and the width of the $$\pi _1$$ π 1 -pole are measured to be $$(1623 \, \pm \, 47 \, ^{+24}_{-75})\, \mathrm {MeV/}c^2$$ ( 1623 ± 47 - 75 + 24 ) MeV / c 2 and $$(455 \, \pm 88 \, ^{+144}_{-175})\, \mathrm {MeV}$$ ( 455 ± 88 - 175 + 144 ) MeV .

2020 ◽  
Vol 6 (2) ◽  
pp. 90-97
Author(s):  
Sagir Masanawa ◽  
Hamza Abubakar

In this paper, a hybrid intelligent system that consists of the sparse matrix approach incorporated in neural network learning model as a decision support tool for medical data classification is presented. The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners to accelerate diagnosis and treatment processes. The sparse matrix approach incorporated in neural network learning algorithm for scalability, minimize higher memory storage capacity usage, enhancing implementation time and speed up the analysis of the medical data classification problem. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. The proposed intelligent classification system maximizes the intelligently classification of medical data and minimizes the number of trends inaccurately identified. To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Hepatitis, SPECT Heart and Cleveland Heart from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity. The results were analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system was effective in undertaking medical data classification tasks.


1980 ◽  
Vol 294 (2) ◽  
pp. 137-147 ◽  
Author(s):  
Gerhard Soff ◽  
Joachim Reinhardt ◽  
Berndt M�ller ◽  
Walter Greiner

1973 ◽  
Author(s):  
G. Grayer ◽  
B. Hyams ◽  
C. Jones ◽  
P. Schlein ◽  
P. Weilhammer ◽  
...  

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