Simple Improved Reference Subtraction for H4RG, H2RG, and H1RG Near-infrared Array Detectors

TL;DR

This paper introduces a simple, optimal method called SIRS for reducing correlated read noise in near-infrared detectors using reference pixels, applicable with existing data and demonstrated on H4RG detector test data.

Contribution

The paper presents a new linear algebra-based method, SIRS, for improved noise correction in near-infrared detectors that is simple, optimal, and compatible with existing data processing pipelines.

Findings

SIRS effectively reduces correlated read noise in H4RG detector data.

The method is optimal in a least squares sense when using embedded reference pixels.

The software implementation is freely available and easy to integrate.

Abstract

Teledyne's H4RG, H2RG, and H1RG near-infrared array detectors provide reference pixels embedded in their data streams. Although they do not respond to light, the reference pixels electronically mimic normal pixels and track correlated read noise. In this paper, we describe how the reference pixels can be used with linear algebra and training data to optimally reduce correlated read noise. Simple Improved Reference Subtraction (SIRS) works with common detector clocking patterns and, when applicable, relies only on post-processing existing data so long as the reference pixels are available. The resulting reference correction is optimal, in a least squares sense, when the embedded reference pixels are the only references and the reference columns on the left and right are treated as two reference streams. We demonstrate SIRS using H4RG ground test data from the Nancy Grace Roman Space Telescope Project. The Julia language SIRS software is freely available for download from the NASA GitHub. The package includes a python-3 ``backend'' that can be used to apply SIRS corrections if a SIRS calibration file has been provided by the instrument builders.