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arXiv 2602.09779astro-ph.GA
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利用人工神经网络分析HSC-SSP高分辨率深场图像中的银河 cirrus 纤维

Analysis of Galactic cirrus filaments in HSC-SSP high-resolution deep images using artificial neural networks

Central (Pulkovo) Astronomical Observatory, Russian Academy of Sciences(俄罗斯科学院中央(普尔科沃)天文台);Saint Petersburg State University(圣彼得堡国立大学);Astrophysical Research Consortium(天体物理研究联盟);University of Washington(华盛顿大学)
Denis M. Poliakov, Anton A. Smirnov, Sergey S. Savchenko, Alexander A. Marchuk, Aleksandr V. Mosenkov, Vladimir B. Ilin, George A. Gontcharov, Daria G. Turichin… 展开作者

Denis M. Poliakov, Anton A. Smirnov, Sergey S. Savchenko, Alexander A. Marchuk, Aleksandr V. Mosenkov, Vladimir B. Ilin, George A. Gontcharov, Daria G. Turichina, Andrey D. Panasyuk

AI总结:

利用人工神经网络分析HSC-SSP高分辨率深场图像中的银河 cirrus 纤维,发现其分布特征及对天空背景估计的影响。

摘要:

银河光学 cirrus 的存在对观测银河系内微弱天体和远紫外星系结构构成了挑战。为了研究Hyper Suprime-Cam Subaru Strategic Program公有数据释放3(HSC-SSP DR3)中的单个cirrus纤维,我们采用基于卷积神经网络和集成学习的技术。这种方法使我们能够区分cirrus纤维与前景和背景物体,整个HSC-SSP范围内均使用g、r和i波段的光学图像。与之前使用深度Sloan Digital Sky Survey Stripe~82(SDSS Stripe~82)数据的工作相比,本研究中识别的cirrus云在重叠调查区域内的位置高度一致。然而,在更深层的HSC-SSP数据集中,我们能够检测到4.5倍更多的cirrus云。我们的研究表明,HSC-SSP coadd图像中的天空背景被过度减去,这通过cirrus纤维和周围区域的表面亮度分布得以证实。表面亮度为m=29 mag arcsec⁻²的靠近大型纤维的物体在r波段中可能被过度减去0.5个星等。这表明在估计天空背景的算法中应考虑cirrus云。在实际应用中,我们提供了纤维的目录以及一个允许训练神经网络模型以分割HSC-SSP coadd图像中cirri的框架。

英文摘要:

The existence of Galactic optical cirrus poses a challenge for observing faint objects within our Galaxy and dim extragalactic structures. To investigate individual cirrus filaments in the Hyper Suprime-Cam Subaru Strategic Program public data release 3 (HSC-SSP DR3) we use a technique based on convolutional neural networks and ensemble learning. This approach allows us to distinguish cirrus filaments from foreground and background objects across the entire HSC-SSP, using optical images in the $g$, $r$, and $i$ wavebands. A comparison with previous work using deep Sloan Digital Sky Survey Stripe~82 (SDSS Stripe~82) data reveals that the cirrus clouds identified in this study are highly consistent in location within the overlapping survey region. However, in the deeper HSC-SSP dataset, we were able to detect $4.5$ times more cirrus clouds. Our study indicates that the sky background in HSC-SSP coadd images is over-subtracted, as evidenced by the surface brightness distribution in cirrus filaments and surrounding regions. Objects with surface brightness of $m = 29~\mbox{mag~arcsec}^{-2}$ near large filaments can be dimmed by over-subtraction of $0.5$ magnitude in the $r$ band. This suggests that cirrus clouds should be taken into account in algorithms for estimating the sky background. For practical use, we provide a catalog of filaments and a framework that allows one to train neural network models for segmenting cirri in HSC-SSP coadd images.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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