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Near-infrared array detectors, like the \JWST NIRSpec's Teledyne's H2RGs,often provide reference pixels and a reference output. These are used to removecorrelated noise. Improved Reference Sampling and Subtraction (\IRSSquare,pronounced "IRS-square") is a statistical technique for using this referenceinformation optimally in a least squares sense. Compared to "traditional" H2RGreadout, \IRSSquare uses a different clocking pattern to interleave many morereference pixels into the data than is otherwise possible. Compared to standardreference correction techniques, \IRSSquare subtracts the reference pixels andreference output using a statistically optimized set of frequency dependentweights. The benefits include somewhat lower noise variance and much lessobvious correlated noise. NIRSpec's \IRSSquare images are cosmetically clean,with less $1/f$ banding than in traditional data from the same system. Thisarticle describes the \IRSSquare clocking pattern and presents the equationsthat are needed to use \IRSSquare in systems other than NIRSpec. For NIRSpec,applying these equations is already an option in the calibration pipeline. Asan aid to instrument builders, we provide our prototype \IRSSquare calibrationsoftware and sample \JWST NIRSpec data. The same techniques are applicable toother detector systems, including those based on Teledyne's H4RG arrays. TheH4RG's "interleaved reference pixel readout" mode is effectively one \IRSSquarepattern.

Improved Reference Sampling and Subtraction: A Technique for Reducing the Read Noise of Near-infrared Detector Systems