用深度视觉技术解析太阳物理大数据，突破传统分析瓶颈
Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges
- 结合深视觉模型处理太阳观测的海量多源数据
- 可实现太阳活动自动识别与复杂结构建模
- 适合天文、人工智能交叉研究者参考
近年来，包括太阳动力学天文台（SDO）和帕克太阳探测器在内的空间望远镜，以及丹尼尔·基·伊努耶太阳望远镜（DKIST）等地面设备，使太阳物理进入大数据时代（SPBD），数据量、速度和多样性急剧增长。随着深度计算机视觉的发展，此前难以解决的太阳物理问题迎来新机遇。然而，由于SPBD固有特性及深度模型的局限性，也带来了新的挑战。本文综述了SPBD的不同类型，探讨了深度视觉在该领域的应用前景，指出了独特挑战，并提出了若干未来研究方向。
原文摘要 · Abstract (English)
With recent missions such as advanced space-based observatories like the Solar Dynamics Observatory (SDO) and Parker Solar Probe, and ground-based telescopes like the Daniel K. Inouye Solar Telescope (DKIST), the volume, velocity, and variety of data have made solar physics enter a transformative era as solar physics big data (SPBD). With the recent advancement of deep computer vision, there are new opportunities in SPBD for tackling problems that were previously unsolvable. However, there are new challenges arising due to the inherent characteristics of SPBD and deep computer vision models. This vision paper presents an overview of the different types of SPBD, explores new opportunities in applying deep computer vision to SPBD, highlights the unique challenges, and outlines several potential future research directions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。