AIの力で宇宙のリングを探す 〜画像認識技術で銀河の泡状構造を効率的に検出〜

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2025-03-19 大阪公立大学

大阪公立大学の研究者らは、AI(人工知能)を活用して流体の動きを予測する新たなモデルを開発し、従来の方法と同等の精度を保ちながら、計算時間を約45分から約3分に大幅短縮することに成功しました。このモデルは、海洋発電設備や船舶の設計時に必要な流体挙動のリアルタイム計算などへの応用が期待されています。

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ディープラーニングを用いた天の川銀河とその周辺の赤外線気泡認識 Infrared bubble recognition in the Milky Way and beyond using deep learning

Shimpei Nishimoto, Toshikazu Onishi, Atsushi Nishimura, Shinji Fujita, Yasutomo Kawanishi, Shuyo Nakatani, Kazuki Tokuda, Yoshito Shimajiri, Hiroyuki Kaneko, Yusuke Miyamoto …
Publications of the Astronomical Society of Japan  Published:18 March 2025
DOI:https://doi.org/10.1093/pasj/psaf008

AIの力で宇宙のリングを探す 〜画像認識技術で銀河の泡状構造を効率的に検出〜

Abstract

We propose a deep-learning model that can detect Spitzer bubbles accurately using two-wavelength near-infrared data acquired by the Spitzer Space Telescope and JWST. The model is based on the single-shot multibox detector as an object detection model, trained and validated using Spitzer bubbles identified by the Milky Way Project (MWP bubbles). We found that using only MWP bubbles with clear structures, along with normalization and data augmentation, significantly improved performance. To reduce the dataset bias, we also use data without bubbles in the dataset selected by combining two techniques: negative sampling and clustering. The model was optimized by hyperparameter tuning using Bayesian optimization. Applying this model to a test region of the Galactic plane resulted in a 98% detection rate for MWP bubbles with 8 µm emission clearly encompassing 24 µm emission. Additionally, we applied the model to a broader area of 1≤|l|≤65⁠, |b|≤1⁠, including both training and validation regions, and the model detected 3006 bubbles, of which 1413 were newly detected. We also attempted to detect bubbles in the high-mass star-forming region Cygnus X, as well as in external galaxies, the Large Magellanic Cloud (LMC) and NGC 628. The model successfully detected Spitzer bubbles in these external galaxies, though it also detected Mira-type variable stars and other compact sources that can be difficult to distinguish from Spitzer bubbles. The detection process takes only a few hours, demonstrating the efficiency in detecting bubble structures. Furthermore, the method used for detecting Spitzer bubbles was applied to detect shell-like structures observable only in the 8 µm emission band, leading to the detection of 469 shell-like structures in the LMC and 143 in NGC 628.

0106流体工学
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