arXiv 1909.112302019-09astro-ph.IMastro-ph.GAcs.CV

用NoiseChisel开拓低表面亮度宇宙

Carving out the low surface brightness universe with NoiseChisel

Instituto de Astrofísica de Canarias（加那利群岛天体物理研究所）；Universidad de La Laguna（拉古纳大学）

Mohammad Akhlaghi

备注：: Invited talk at IAU Symposium 355 (The Realm of the Low Surface Brightness Universe). The downloadable source (on arXiv) includes the full reproduction info in Maneage: http://maneage.org . It is also available with its Git history in https://gitlab.com/makhlaghi/iau-symposium-355 (archived in SoftwareHeritage), and in Zenodo at https://doi.org/10.5281/zenodo.6529419
发表信息：: Proceedings of the International Astronomical Union (S355), 2020

摘要：

NoiseChisel是一个程序，用于检测非常低的信噪比（S/N）特征，并对其形态进行最小假设。它于2015年推出，并在称为GNU Astronomy Utilities（Gnuastro）的数据分析程序和库集合中发布。Gnuastro的第10个稳定版本于2019年8月发布，NoiseChisel有了显着改进：检测更微弱的信号，使用户能够更好地控制其内部工作，并修复了许多错误。在0.10版本之前最重要的变化是NoiseChisel的分割功能已经被移动到一个名为Segment的新程序中。另一个主要的变化是其真实探测的最终增长策略，例如NoiseChisel能够在单次曝光的SDSS图像（r波段）上探测到信噪比为0.25或25.97 mag/arcsec 2的M51的外翼。Segment还能够更成功地将局部HII区域检测为“团块”。有关0.10版之后的改进的详细列表，请参阅最新的手册。最后，为了协调受控分析，讨论了再现性的概念：这篇论文本身是完全可再现的（commit 751467 d）。

英文摘要：

NoiseChisel is a program to detect very low signal-to-noise ratio (S/N) features with minimal assumptions on their morphology. It was introduced in 2015 and released within a collection of data analysis programs and libraries known as GNU Astronomy Utilities (Gnuastro). The 10th stable version of Gnuastro was released in August 2019 and NoiseChisel has significantly improved: detecting even fainter signal, enabling better user control over its inner workings, and many bug fixes. The most important change until version 0.10 is that NoiseChisel's segmentation features have been moved into a new program called Segment. Another major change is the final growth strategy of its true detections, for example NoiseChisel is able to detect the outer wings of M51 down to S/N of 0.25, or 25.97 mag/arcsec2 on a single-exposure SDSS image (r-band). Segment is also able to detect the localized HII regions as "clumps" much more successfully. For a detailed list of improvements after version 0.10, see the most recent manual. Finally, to orchestrate a controlled analysis, the concept of reproducibility is discussed: this paper itself is exactly reproducible (commit 751467d).

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。