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arXiv 2306.03250astro-ph.IM
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NSClean:一种从JWST NIRSpec图像中去除相关噪声的算法

NSClean: An Algorithm for Removing Correlated Noise from JWST NIRSpec Images

Bernard J. Rauscher

AI总结:

NSClean是一种用于去除JWST NIRSpec图像中微弱垂直条带和画框噪声的算法,通过傅里叶空间背景建模实现高效、均匀的噪声去除,计算开销低,且软件包免费公开。

摘要:

NSClean是一种算法及相关的Python软件包,用于从詹姆斯·韦伯太空望远镜(JWST)近红外光谱仪(NIRSpec)图像中去除微弱的垂直条带和“画框噪声”。NSClean利用已知的暗区域,在傅里叶空间中对每次曝光拟合背景模型。当模型被减去时,几乎所有的相关噪声都被去除。与减去滚动中位数等更简单的策略相比,NSClean更为彻底和均匀。NSClean的计算要求不高,在典型的笔记本电脑上仅需几秒钟即可清理一幅图像。NSClean软件包可从NASA JWST网站(https://webb.nasa.gov/content/forScientists/publications.html)免费获取。

英文摘要:

NSClean is an algorithm and associated python package for removing faint vertical banding and ``picture frame noise'' from JWST Near Infrared Spectrograph (NIRSpec) images. NSClean uses known dark areas to fit a background model to each exposure in Fourier space. When the model is subtracted, it removes nearly all correlated noise. Compared to simpler strategies like subtracting the rolling median, NSClean is more thorough and uniform. NSClean is computationally undemanding, requiring only a few seconds to clean an image on a typical laptop. The NSClean package is freely available from the NASA JWST website (https://webb.nasa.gov/content/forScientists/publications.html).

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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