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arXiv · 2301.00415

PCA-based Data Reduction and Signal Separation Techniques for James-Webb Space Telescope Data Processing

Güray Hatipoğlu
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Abstract

Principal Component Analysis (PCA)-based techniques can separate data into different uncorrelated components and facilitate the statistical analysis as a pre-processing step. Independent Component Analysis (ICA) can separate statistically independent signal sources through a non-parametric and iterative algorithm. Non-negative matrix factorization is another PCA-similar approach to categorizing dimensions in physically-interpretable groups. Singular spectrum analysis (SSA) is a time-series-related PCA-like algorithm. After an introduction and a literature review on processing JWST data from the Near-Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI), potential parts to intervene in the James Webb Space Telescope imaging data reduction pipeline will be discussed.

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Güray Hatipoğlu. 2023-01-01. PCA-based Data Reduction and Signal Separation Techniques for James-Webb Space Telescope Data Processing. https://arxiv.org/abs/2301.00415

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