Machine learning-based analysis has become essential to efficiently handle the increasing massive data from modern astronomical instruments in recent years. Churchwell et al. (2006, 2007) identified infrared ring structures, which are believed to relate to the formation of massive stars, with the human eye. Recently, Ueda et al. (2020) showed that Convolutional Neural Networks (CNN) can detect objects with indistinct boundaries such as infrared rings with comparable accuracy as the human eye. However, such a classification-based object detector requires a long processing time, making it impractical to apply to existing all-sky 12 μm and 22 μm data captured by WISE. We introduced the Single Shot MultiBox Detector (SSD, Liu W. et al. 2016), which directly outputs the locations and confidences of targets, to significantly reduce the time for identification. We applied an SSD model to the rings toward the 6 deg2 region in the Galactic plane which is the same region used in Ueda et al. (2020), and confirmed that the time for identification was reduced by about 1/80 with maintaining almost the same accuracy. Since detecting small rings is still difficult by even this model, an input image should be cropped
into small images, which increases the number of applications of the model. There is still room for reducing the
processing time. In the future, we will try to solve this problem and detect the rings faster.
We report the current status of the 1.85-m mm-submm telescope installed at the Nobeyama Radio Observatory (altitude 1400 m) and the future plan. The scientific goal is to reveal the physical/chemical properties of molecular clouds in the Galaxy by obtaining large-scale distributions of molecular gas with an angular resolution of several arcminutes. A semi-automatic observation system created mainly in Python on Linux-PCs enables effective operations. A large-scale CO J =2–1 survey of the molecular clouds (e.g., Orion-A/B, Cygnus-X/OB7, Taurus- California-Perseus complex, and Galactic Plane), and a pilot survey of emission lines from minor molecular species toward Orion clouds have been conducted so far. The telescope also is providing the opportunities for technical demonstrations of new devices and ideas. For example, the practical realizations of PLM (Path Length Modulator) and waveguide-based sideband separating filter, installation of the newly designed waveguide-based circular polarizer and OMT (Orthomode Transducer), and so on. As the next step, we are now planning to relocate the telescope to San Pedro de Atacama in Chile (altitude 2500 m), and are developing very wideband receiver covering 210–375 GHz (corresponding to Bands 6–7 of ALMA) and full-automatic observation system. The new telescope system will provide large-scale data in the spatial and frequency domain of molecular clouds of Galactic plane and Large/Small Magellanic Clouds at the southern hemisphere. The data will be precious for the comparison with those of extra-galactic ones that will be obtained with ALMA as the Bands 6/7 are the most efficient frequency bands for the surveys in extra-galaxies for ALMA.
Recently, the amount of data obtained from astronomical instruments has been increasing explosively, and data science methods such as Machine Learning/Deep Learning gain attention on the back of the growth in demand for automatic analysis. Using these methods, the number of applications to the target sources that have clear boundaries with the background i.e., stars, planets, and galaxies is increasing year by year. However, there are a few studies which applied the data science methods to the interstellar medium (ISM) distributed in the Galactic plane, which have complicated and ambiguous silhouettes. We aim to develop classifiers to automatically extract various structures of the ISM by Convolutional Neural Network (CNN) that is strong in image recognition even in deep learning. In this study, we focus on the infra-red (IR) ring structures distributed in the Galactic plane. Based on the catalog of Churchwell et al. (2006, 2007), we created a “Ring” dataset from the Spitzer/GLIMPSE 8 μm and Spitzer/MIPSGAL 24 μm data and optimized the parameters of the CNN model. We applied the developed model to a range of 16.5° ≤ l ≤ 19.5°, |b| ≤ 1° . As a result, 234 “Ring” candidates are detected. The “Ring” candidates were matched with 75% Milky Way Project (MWP, Simpson et al. 2012) “Ring” and 65% WISE Hii region catalog (Anderson et al. 2014). In addition, new“Ring”and Hii region candidate objects were also found. For these results, we conclude that the CNN model may have a recognition accuracy equal to or better than that of human eyes.
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