Likelihood-based Jump Detection and Cosmic Ray Rejection for Detectors Read Out Up-the-ramp

Abstract

This paper implements likelihood-based jump detection for detectors read out up-the-ramp, using the entire set of reads to compute likelihoods. The approach compares the χ 2 value of a fit with and without a jump for every possible jump location. I show that this approach can be substantially more sensitive than one that only uses the difference between sequential groups of reads, especially for long ramps and for jumps that occur in the middle of a group of reads. It can also be implemented for a computational cost that is linear in the number of resultants. I provide and describe a pure Python implementation that can process a 10-resultant ramp on a 4096 × 4096 detector in ≈20 s, including iterative cosmic ray detection and removal, on a single core of a 2020 Macbook Air. This Python implementation, together with tests and a tutorial notebook, are available at https://github.com/t-brandt/fitramp. I also provide tests and demonstrations of the full ramp fitting and cosmic ray rejection approach on data from the JWST.

Cosmic Ray Jump Detection for the Roman Wide Field Instrument

“…An example, which indeed performs better in read noise dominated regime, is the Y-intercept method of Anderson & Gordon (2011). More recently, Brandt (2024) present such a method where a global χ 2 is computed by omitting one resultant difference at a time and fitting a common slope to the rest of the resultant differences. We will see in the following that for the expected Roman WFI parameters, CR are easy to detect in the read noise dominated regime, so we do not pursue goodness-of-fit based method further in this paper.…”