Measurements of the cosmic-ray hydrogen and helium spectra at energies from 20 to 800 TeV are presented. The experiments were performed on a series of twelve balloon Ñights, including several long duration Australia to South America and Antarctic circumpolar Ñights. No clear evidence is seen for a spectral break. Both the hydrogen and the helium spectra are consistent with power laws over the entire energy range, with integral spectral indices 1.80^0.04 and for the protons and helium, respec-1.68~0 .06 0.04 tively. The results are fully consistent with expectations based on supernova shock acceleration coupled with a "" leaky box ÏÏ model of propagation through the Galaxy.

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[1] Cosmic ray muon radiography can measure the density distribution within a volcano. Unidirectional radiography shows a precise cross-sectional view of a conduit and a magma body through a volcano parallel to the plane of the detector. However, it only resolves the average density distribution along individual muon paths. Precise size and shape of underground structure, such as a conduit or a magma body, provide clear and pervasive information on understanding dynamics of volcanic eruption. Here we show a highly resolved three-dimensional tomographic image of an active volcano Asama in Japan. Specifically, we developed a portable power-effective muon radiography telescope that can be operated stable with a realistically sized solar panel so as to place it around an active volcano where commercial electric power is not available. The resulting image below the crater floor shows that a local low-density region accumulates sufficient gas pressure to cause Vulcanian eruption. The present muon computational axial tomography scan has a resolving power with a resolution of 100 m, allowing it to see great detail in volcanoes.

Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.