Prediction of high energy particle shower sizes and core location using artificial neural networks

被引：7
|
作者：
Devi, G. [1 ]
Sarma, K. K. [2 ]
Datta, P. [2 ]
Mahanta, A. K. [3 ]
机构：
[1] Lalit Chandra Bharali Coll, Deptarment Phys, Gauhati 781011, Assam, India
[2] Gauhati Univ, Deptarment Elect & Commun Technol, Gauhati 781014, Assam, India
[3] Gauhati Univ, Deptarment Comp Sci, Gauhati 781014, Assam, India
来源：
INDIAN JOURNAL OF PHYSICS | 2012年 / 86卷 / 01期
关键词：
EAS; Core; Size; Location; ANN; MLP; EXTENSIVE AIR-SHOWERS;
D O I：
10.1007/s12648-012-0007-4
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Traditionally Artificial Neural Network (ANN)s, as non-parametric prediction tools, are used for pattern recognition and related applications but can be modeled for High Energy Particle shower size and core location prediction. The present work describes the use of an ANN based system which has been configured to predict shower sizes in the range 10(10.5)-10(20.5) eV and core locations of twenty events. The system receives density values as inputs and generates shower sizes for 20 core positions and predicts the coordinates of the locations. The results derived with variations of input upto 50% show success rates in the range of 90s.
引用
收藏
页码：77 / 84
页数：8
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