Analysis of Galactic cirrus filaments in HSC-SSP high-resolution deep images using artificial neural networks
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914366065999872 |
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| author | Poliakov, Denis M. Smirnov, Anton A. Savchenko, Sergey S. Marchuk, Alexander A. Mosenkov, Aleksandr V. Ilin, Vladimir B. Gontcharov, George A. Turichina, Daria G. Panasyuk, Andrey D. |
| author_facet | Poliakov, Denis M. Smirnov, Anton A. Savchenko, Sergey S. Marchuk, Alexander A. Mosenkov, Aleksandr V. Ilin, Vladimir B. Gontcharov, George A. Turichina, Daria G. Panasyuk, Andrey D. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_09779 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Analysis of Galactic cirrus filaments in HSC-SSP high-resolution deep images using artificial neural networks Poliakov, Denis M. Smirnov, Anton A. Savchenko, Sergey S. Marchuk, Alexander A. Mosenkov, Aleksandr V. Ilin, Vladimir B. Gontcharov, George A. Turichina, Daria G. Panasyuk, Andrey D. Astrophysics of Galaxies J.2; I.4.6; G.3 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. |
| title | Analysis of Galactic cirrus filaments in HSC-SSP high-resolution deep images using artificial neural networks |
| topic | Astrophysics of Galaxies J.2; I.4.6; G.3 |
| url | https://arxiv.org/abs/2602.09779 |