Nonlinearity in H4RG-10 Near-Infrared Detectors at Elevated Temperatures: Characterization and Data-Driven Correction Method

TL;DR

This paper identifies a new nonlinearity in H4RG-10 near-infrared detectors at elevated temperatures and introduces a data-driven correction method that significantly improves linearity, especially for pixels with high defect currents.

Contribution

The paper characterizes a previously unreported nonlinearity in H4RG-10 detectors and develops a novel data-driven correction approach using dual-illumination and dark measurements.

Findings

Significant nonlinearity arises from defect currents at elevated temperatures.

The proposed correction method improves linearity for high-defect pixels.

Reliable linearity correction achieved across the detector array.

Abstract

We report a newly identified nonlinearity in H4RG-10 near-infrared detectors operating under moderately elevated-temperature conditions (114 K). This component, that potentially arises from illumination-independent defect currents, introduces additional nonlinearity not captured by conventional correction models. To address this issue, we propose a data-driven nonlinearity correction (NLC) method that models the nonlinear behavior of both the classical response and the defect currents, using dual-illumination measurements and a dark exposure. Applied to H4RG-10 detectors on the PRIME telescope, the method significantly improves signal linearity, especially for pixels with high defect current, while maintaining comparable performance elsewhere. By selecting the optimal correction model per pixel, reliable NLC is achieved across the full array. This study characterizes a nonlinearity intrinsic to H4RG-10 detectors and demonstrates that data-driven post-processing can effectively restore linearity in the presence of large defect currents. Although these effects are unlikely to be significant under nominal operating temperatures, the approach may provide a practical calibration framework for future warm-operation scenarios.