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東京大学・天文学教育研究センターでは2003年4月から談話会を開いています。
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ML: semiadm _at_ ioa.s.u-tokyo.ac.jp
please replace _at_ with @
Language: Japanese
Abstract: Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as the distance to a molecular cloud. However, there is a problem in that for the inner Galaxy, two different solutions, the “Near" solution, and the “Far" solution, can be derived simultaneously. We attempted to construct a two-class ( “Near” or “Far” ) inference model using a Convolutional Neural Network (CNN), a form of deep learning that can capture spatial features generally. In this study, we used the CO dataset toward the 1st quadrant of the Galactic plane obtained with the Nobeyama 45-m radio telescope (l = 62-10 degree, |b| < 1 degree). In the model, we applied the three-dimensional distribution (position-position-velocity) of the 12CO (J=1-0) emissions as the main input. The dataset with “Near” or “Far” annotation was made from the HII region catalog of the infrared astronomy satellite WISE to train the model. As a result, we could construct a CNN model with a 76% accuracy rate on the training dataset. By using the model, we determined the distance to molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of the molecular clouds with a distance of < 16.3 kpc identified in the 12CO data follows a power-law distribution with an index of about from -1.5 to -2.3 in the mass range of M >1000 Msun. In particular, the slope was shallow in the arm region and the bar-end region. Also, the detailed molecular gas distribution of the Galaxy as seen from the Galactic North pole was determined. In addition, we obtained a result that approximately 450 cloud-cloud collision events are expected to be included in the data.
| # | Date | Speaker | Title | Chair |
| 403 | 2023年6月8日(木) | 今井正尭 (IoA/UT) | TBA | 天文センター |
| 404 | 2023年6月29日(木) | 谷口琴美 (国立天文台・科学研究部) | TBA | 天文センター |
詳細はこちら: 令和5(2023)年度談話会
| # | Date | Speaker | Title | Chair |
| 401 | 2023年4月20日(木) | Yao-Lun Yang (Research Scientist, RIKEN) | Complex chemistry in the era of JWST and ALMA | K. Kohno |
| 400 | 2023年4月6日(木) | Doug Johnstone (National Research Council Canada) | What the Variability of Embedded Protostars Tells Us about Accretion: Past, Present, and Future | K. Kohno |
