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

IF 3.3 3区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Publications of the Astronomical Society of the Pacific Pub Date : 2024-01-30 DOI:10.1088/1538-3873/ad1b36

Bernard J. Rauscher

引用次数: 0

Abstract

NSClean is an algorithm and 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 has been developed and tested for NIRSpec IFU mode data, although it can be used on other NIRSpec modes as well. 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.

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NSClean：从 JWST NIRSpec 图像中去除相关噪声的算法

NSClean 是一种算法和 python 软件包，用于去除 JWST 近红外摄谱仪（NIRSpec）图像中微弱的垂直条带和 "画框噪声"。NSClean 使用已知暗区在傅立叶空间中为每次曝光拟合一个背景模型。当减去该模型时，它几乎可以去除所有相关噪声。与减去滚动中值等简单策略相比，NSClean 更彻底、更均匀。NSClean 是针对 NIRSpec IFU 模式数据开发和测试的，但也可用于 NIRSpec 的其他模式。NSClean 的计算要求不高，在普通笔记本电脑上只需几秒钟就能完成图像清理。NSClean 软件包可从 NASA JWST 网站免费获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Publications of the Astronomical Society of the Pacific 地学天文-天文与天体物理

CiteScore

6.70

自引率

5.70%

发文量

103

审稿时长

4-8 weeks

期刊介绍： The Publications of the Astronomical Society of the Pacific (PASP), the technical journal of the Astronomical Society of the Pacific (ASP), has been published regularly since 1889, and is an integral part of the ASP''s mission to advance the science of astronomy and disseminate astronomical information. The journal provides an outlet for astronomical results of a scientific nature and serves to keep readers in touch with current astronomical research. It contains refereed research and instrumentation articles, invited and contributed reviews, tutorials, and dissertation summaries.