We present an archive system named "Adria", which have been developed and maintained by ALMA project team of National Astronomical Observatory of Japan (NAOJ). Adria aims to store and open to the public various science data. Adria is composed of an object storage to store the observation data, the access control by "ticket", JSON format metadata, JavaScript APIs and html documents. The combination has the advantages of flexibility and solidity, which are important to store various telescope data in the same platform and to be maintained with small cost for a long time. Firstly, we have applied Adria to the observation data (since July 2013) of Nobeyama Radio Observatory (NRO) 45m, and then we have added the data (since June 2019) of Atacama Submillimeter Telescope Experiment (ASTE) to the same platform.
We report the current status of the NASCO (NAnten2 Super CO survey as legacy) project which aims to provide all-sky CO data cube of southern hemisphere using the NANTEN2 4-m submillimeter telescope installed at the Atacama Desert through developing a new multi-beam receiver and a new telescope control system. The receiver consists of 5 beams. The four beams, located at the four corners of a square with the beam separation of 720′′, are installed with a 100 GHz band SIS receiver having 2-polarization sideband-separation filter. The other beam, located at the optical axis, is installed with a 200 GHz band SIS receiver having 2-polarization sideband-separation filter. The cooled component is modularized for each beam, and cooled mirrors are used. The IF bandwidths are 8 and 4 GHz for 100 and 200 GHz bands, respectively. Using XFFTS spectrometers with a bandwidth of 2 GHz, the lines of 12CO, 13CO, and C18O of J=1−0 or J=2−1 can be observed simultaneously for each beam. The control system is reconstructed on the ROS architecture, which is an open source framework for robot control, to enable a flexible observation mode and to handle a large amount of data. The framework is commonly used and maintained in a robotic field, and thereby reliability, flexibility, expandability, and efficiency in development are improved as compared with the system previously used. The receiver and control system are installed on the NANTEN2 telescope in December 2019, and its commissioning and science verification are on-going. We are planning to start science operation in early 2021.
We are promoting the Hybrid Installation Project in Nobeyama, Triple-band Oriented (HINOTORI), a project aiming at triple-band simultaneous single-dish and VLBI observation in the 22-, 43- and 86-GHz bands using the Nobeyama 45-m Telescope. The triple-band simultaneous observation becomes possible by developing two perforated plates and mounting them in the Nobeyama 45-m Telescope optics. One is a 22/43-GHz-band perforated plate, which transmits the higher frequency (43-GHz) band and reflects the lower frequency (22-GHz) band, and the other is a 43/86-GHz-band perforated plate, which transmits the 86-GHz band and reflects the 43-GHz band or lower. Both plates are designed to be installed in the large telescope optics with a beam diameter as large as 50 cm and insertion/reflection losses are both 0.22 dB (5%) or less in the design. The receivers used in triple-band simultaneous observation system are the H22 and H40 receivers, which are already installed in the Nobeyama 45-m Telescope, and the TZ receiver, which is a 100-GHz-band receiver including the 86-GHz band and reinstalled in the Nobeyama 45-m Telescope. A system of simultaneous observations in the 22- and 43-GHz bands of the Nobeyama 45-m Telescope with the 22/43- GHz-band perforated plate has been completed and commissioned for scientific observations. Also VLBI fringes between the Nobeyama 45-m telescope with the dual-band observation system and the VERA 20-m telescopes at 22 and 43 GHz was detected successfully.
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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