A study of automatic measurement of emulsion chamber data

我们研究了用图像扫描仪测量高山乳胶室实验观测到的高能宇宙线族的可能性。结果表明：1)用图像扫描仪测量得到的像素值转换成的X射线胶片上的光斑暗度与用普通显微光谱仪测量得到的光斑暗度之间有很好的相关性。2)应用图像处理方法，识别出簇射斑候选。在这些候选点中，真正的阵雨点是根据几个标准来选择的，这些标准表征了真正的阵雨点，即一个点内局部黑暗的分布。在此过程中，噪声点的数量急剧减少，约为1/(10~30)。但在胶片四分之一的区域内仍有约40个噪声点；相比之下，真实的簇射点的数量为~20个。我们还应用人工神经网络在候选点中识别真实的噪声点，但一些真实的簇射点是…更多地被识别为噪声点，反之亦然。有必要考虑另一种输入参数来表征真实的阵雨点。然而，通过应用以下跟踪方法，可以提取大气家族中的真实簇斑和成员簇斑。3)我们应用了两种不同的方法来跟踪连续层中的簇斑。一种是利用X光胶片中簇射斑点的几何形状信息。如果我们可以手动指定至少一个淋浴轨迹，则通过比较连续层中每个胶片中任何两个淋浴斑点的相对距离，可以自动识别其他淋浴轨迹。我们还将Hopfield型神经网络应用到人工事件中，以研究将其用于轨迹寻找问题的可能性。研究发现，即使在存在噪声点和胶片布景不准确的情况下，网络也能给出几乎正确的跟踪，但当包含噪声点时，它们会给出额外的虚假跟踪。较少

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