Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp

TL;DR

This paper presents an optimal, unbiased method for fitting and analyzing up-the-ramp detector readouts, including a Python implementation that efficiently handles large datasets and provides a goodness-of-fit metric.

Contribution

It introduces a general, closed-form, computationally efficient algorithm for optimal ramp fitting with bias correction, applicable to any readout scheme.

Findings

Provides closed-form expressions for optimal fit and bias correction.

Achieves fast processing of large detector data in Python.

Includes a Python implementation with practical performance benchmarks.

Abstract

This paper derives the optimal fit to a pixel's count rate in the case of an ideal detector read out nondestructively in the presence of both read and photon noise. The approach is general for any readout scheme, provides closed-form expressions for all quantities, and has a computational cost that is linear in the number of resultants (groups of reads). I also derive the bias of the fit from estimating the covariance matrix and show how to remove it to first order. The ramp-fitting algorithm I describe provides the $\chi^2$ value of the fit of a line to the accumulated counts, which can be interpreted as a goodness-of-fit metric. I provide and describe a pure Python implementation of these algorithms that can process a 10-resultant ramp on a $4096 \times 4096$ detector in $\approx$8 seconds with bias removal on a single core of a 2020 Macbook Air. This Python implementation, together with tests and a tutorial notebook, are available at \url{https://github.com/t-brandt/fitramp}. A companion paper describes a jump detection algorithm based on hypothesis testing of ramp fits and demonstrates all algorithms on data from JWST.