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

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 $1/f$ banding than in traditional data from the same system. This article describes the \IRSSquare clocking pattern and presents the equations that are needed to use \IRSSquare in systems other than NIRSpec. For NIRSpec, applying these equations is already an option in the calibration pipeline. As an aid to instrument builders, we provide our prototype \IRSSquare calibration software and sample \JWST NIRSpec data. The same techniques are applicable to other detector systems, including those based on Teledyne's H4RG arrays. The H4RG's "interleaved reference pixel readout" mode is effectively one \IRSSquare pattern.