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

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.

• Publication year

  2023

• Venue

  Publications of the Astronomical Society of the Pacific

• Publication date

  2023-06-05

• Fields of study

  Physics, Environmental Science

• Identifiers
  DOI 10.1088/1538-3873/ad1b36arXiv 2306.03250
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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