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.
Photogrammetry technique is widely used for the initial alignment of main-reflector panels of millimeter/ submillimeter-wave telescopes by analyzing a great number of photos of the reflector at the rest state taken from different angles and distances. In this study, we investigated a possibility that the photogrammetry can be applied for real-time surface measurements which is important to realize active surface controls that improve reflector surface accuracy during scientific observations. The technique is important especially for realizing larger aperture and higher frequency telescopes. We developed a simulator to investigate the accuracy of the surface measurements with photos taken with fixed cameras mounted on the stays of the sub-reflector. As a result, we found that the accuracy of surface measurement is roughly inversely proportional to square-root of the number of fixed cameras, and the calculation time roughly proportional to the product of the numbers of cameras and measurement points. For the case of Nobeyama 45-m telescope, the accuracy of 1 mm (rms) was achieved for 164 surface points by 10 cameras with a calculation time of ∼2 sec by a developed python code using a single-core Xeon processor. In order to improve the accuracy with a minimum number of cameras, more various camera positions (e.g., surrounding the vertex hole of the main reflector and surrounding the main reflector) should be investigated, and their combination should be optimized. Applying high-performing technologies such as multiprocessors and/or GPUs, faster calculation is to be considered.
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