Nonlinearity Correction Algorithm Processes 4096×4096 Detectors from 186 Ramps in ~10,000 Reads Per Pixel
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Detectors used in modern astronomy routinely suffer from nonlinearities, distorting the measurement of faint astronomical signals, and accurate correction of these effects is crucial for obtaining reliable data. Timothy D. Brandt from the Space Telescope Science Institute, along with colleagues, now presents a new algorithm to address this longstanding problem, specifically designed for detectors that measure light incrementally, or “up-the-ramp”. This method efficiently determines the precise mathematical relationship between the raw detector readings and the true light intensity, even in the presence of electronic noise, and importantly, provides a way to assess when further refinement of the correction offers no significant benefit.
The team demonstrates the algorithm’s effectiveness on simulated data from the Roman Space Telescope’s Wide Field Instrument, revealing that a high degree of nonlinearity correction is necessary to achieve optimal results, and establishing a new standard for detector calibration in future astronomical observations. Non-linearity arises when a detector’s response to light isn’t directly proportional to the incoming signal, introducing systematic errors. The algorithm models detector non-linearity using a polynomial function and directly calculates the necessary correction coefficients, streamlining the process by avoiding initial estimations of signal levels or inverse corrections. The algorithm’s efficiency is notable, scaling linearly with the number of readings and only weakly with the polynomial order, making it suitable for processing the large datasets generated by modern, large-format detectors. This work addresses non-linearity alongside read and photon noise, simultaneously analyzing multiple detector measurements taken at varying light levels to directly determine the function transforming measured counts into linearized values. The algorithm’s efficiency is notable, requiring approximately 100 hours on a standard laptop to correct the entire 4096×4096 pixel array of a Hawaii-4 RG detector using a substantial set of measurements. To rigorously assess the correction, the team developed a method for determining the optimal polynomial degree, using a statistical technique to identify the point beyond which increasing the polynomial order yields no significant improvement. They tested the algorithm’s performance under controlled conditions by generating synthetic data with known non-linearity and incorporating realistic noise, allowing for a direct comparison between original and corrected data. The researchers also explored potential biases arising from combining measurements with significantly different illumination levels, developing strategies to ensure the robustness of the correction across a wider range of observational conditions.
Nonlinear Detector Correction via Matrix Reconstruction Scientists have developed a new algorithm to correct for non-linearities in detector readings, a crucial step for achieving accurate measurements in astronomical observations and other fields. This work addresses distortions that arise when measuring light, ensuring that recorded counts accurately reflect the incoming signal. The algorithm operates on multiple detector measurements simultaneously, directly calculating the function needed to transform measured counts into linearized values. The method involves constructing a complex matrix, derived from the collected data, to model the non-linear distortions, accounting for variations in illumination and noise. A key innovation is the inclusion of a constraint that stabilizes the calculation, preventing the algorithm from producing trivial solutions. By fixing the sum of the signal levels across all measurements, the team ensures a robust and meaningful correction is achieved. The algorithm’s efficiency is linear with the number of measurements, meaning that processing time increases proportionally with the amount of data.
The team developed a method that directly calculates the function transforming measured counts into linearized counts, operating efficiently on multiple detector readings simultaneously. The algorithm’s computational cost scales linearly with the number of readings and is manageable even for high-order polynomial corrections, demonstrated by a test on a large detector array taking approximately 100 hours on standard laptop hardware. The study also identified a potential bias when applying the correction to data with widely varying illumination levels, and proposed effective mitigation strategies. The software implementing this algorithm is publicly available, facilitating its adoption by the wider astronomical community. 👉 More information 🗞 A Classic Nonlinearity Correction Algorithm for Detectors Read Out Up-The-Ramp 🧠 ArXiv: https://arxiv.org/abs/2512.09132 Tags: Rohail T. As a quantum scientist exploring the frontiers of physics and technology. My work focuses on uncovering how quantum mechanics, computing, and emerging technologies are transforming our understanding of reality. I share research-driven insights that make complex ideas in quantum science clear, engaging, and relevant to the modern world. Latest Posts by Rohail T.: Llms in Interpreting Legal Documents Demonstrate Potential for Optimising Tasks and Navigating Emerging Regulations December 12, 2025 Dynamic Stimulated Emission Enables Deterministic Photon Addition and Subtraction with 99% Fidelity December 12, 2025 Human-ai Interactive Theorem Proving Enables Scientific Discovery and Preserves Mathematical Rigor December 12, 2025