Improved Reference Sampling and Subtraction: A Technique for Reducing the Read Noise of Near-infrared Detector Systems
Bernard J. Rauscher, Richard G. Arendt, D.J. Fixsen, Matthew A., Greenhouse, Matthew Lander, Don Lindler, Markus Loose, S.H. Moseley, D. Brent, Mott, Yiting Went, Donna V. Wilson, Christos Xenophontos

TL;DR
This paper introduces extIRSSquare, a statistical technique that optimizes reference pixel subtraction in near-infrared detectors, reducing read noise and correlated noise, demonstrated with JWST NIRSpec data and applicable to other systems.
Contribution
The paper presents extIRSSquare, a novel clocking pattern and statistical method for improved noise reduction in near-infrared detector systems, including software tools and equations for broader application.
Findings
Lower noise variance achieved in NIRSpec data.
Significant reduction in correlated 1/f noise.
Applicable to other detector systems like H4RG.
Abstract
Near-infrared array detectors, like the \JWST NIRSpec's Teledyne's H2RGs, often provide reference pixels and a reference output. These are used to remove correlated noise. Improved Reference Sampling and Subtraction (\IRSSquare, pronounced "IRS-square") is a statistical technique for using this reference information optimally in a least squares sense. Compared to "traditional" H2RG readout, \IRSSquare uses a different clocking pattern to interleave many more reference pixels into the data than is otherwise possible. Compared to standard reference correction techniques, \IRSSquare subtracts the reference pixels and reference output using a statistically optimized set of frequency dependent weights. The benefits include somewhat lower noise variance and much less obvious correlated noise. NIRSpec's \IRSSquare images are cosmetically clean, with less banding than in traditional data…
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