2004 Fiscal Year Final Research Report Summary
A study of automatic measurement of emulsion chamber data
| Project/Area Number |
15540291
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Particle/Nuclear/Cosmic ray/Astro physics
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| Research Institution | Kinki University |
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Principal Investigator |
TAMADA Masanobu Kinki University, School of Science & Engineering, Professor, 理工学部, 教授 (70163673)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMANO Noriyuki Kinki University, School of Science & Engineering, Professor, 理工学部, 教授 (10257975)
HIRONAGA Mikiya Kinki University, School of Science & Engineering, Assistant, 理工学部, 助手 (20257976)
HONDA Ken Yamanashi University, Graduate School of Medical & Engineering, Science Dept.of Research, Professor, 大学院・医学工学総合研究部, 教授 (10115321)
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| Project Period (FY) |
2003 – 2004
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| Keywords | Cosmic Rays / Emulsion Chamber / Nuclear Interaction / Image Processing / Pattern Recognition / Neural Networks |
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| Research Abstract |
We have studied a possibility to use image scanner for measuring high energy cosmic-ray families observed by the high mountain emulsion chamber experiments. The results are summarized as follow.
1)There are very good correlation between spot darkness in the X-ray films transformed from pixel value obtained by the measurement using image scanner and that measured by using usual micro-photometer.
2)Applying image processing method, candidates of shower-spots are identified. Among those candidates spots, true shower-spots are selected according to several criteria which characterize true shower-spots, i.e., a distribution of local darkness inside a spot. The number of noise spots decreases drastically, 〜1/(10〜30) in this procedure. But there still remain 〜40 noise spots in the area of one quarter of the film ; in contrast the number of true shower-spots is 〜20. We also applied artificial neural network to identify the true spots among candidates spots, but some of the true shower-spots were
… More
identified to be noise spots and vice versa. It is necessary to consider another input parameters to characterize true shower-spots. It is, however, possible to pick up true shower-spots and member shower-spots in a atmospheric family by applying the following tracking method.
3)We applied two different methods for tracking of shower-spots in successive layers. One is to use information on geometrical configuration of shower-spots in the X-ray films. If we can manually specify at least one shower track, then the other shower tracks are automatically identified by comparing relative distances of any two shower spots in each film in the successive layers. We applied also the Hopfield-type neural networks to the artificial events in order to study a possibility to use it for the track finding problem. It is found that the networks give practically correct tracking even when there exist noise spots and inaccuracy of film setting, though they give additional fake tracking when noise spots are included. Less
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