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

TL;DR

This paper employs convolutional neural networks to identify Galactic cirrus filaments in HSC-SSP images, revealing more clouds than previous surveys and highlighting sky background over-subtraction issues affecting faint object detection.

Contribution

It introduces a neural network-based method for detecting Galactic cirrus in high-resolution images and provides a comprehensive catalog and training framework for future studies.

Findings

Detected 4.5 times more cirrus clouds in HSC-SSP than in SDSS Stripe 82.

Identified over-subtraction of sky background affecting faint object measurements.

Provided a neural network training framework and filament catalog for the astronomical community.

Abstract

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.