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 ${\chi }^2$ 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 $4096\times 4096$ detector in $\approx$20 seconds, 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 \url{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 JWST.

arXiv comment17 pages, 10 figures, PASP accepted. Code available at https://github.com/t-brandt/fitramp. This submission supersedes Sections 6 and 7 and Appendix C of arXiv:2309.08753v1. arXiv admin note: substantial text overlap with arXiv:2309.08753

References in corpus (12)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• The NumPy array: a structure for efficient numerical computation
• Cosmic-Ray Rejection by Laplacian Edge Detection
• A Fast Algorithm for Cosmic Rays Removal from Single Images
• Teledyne H1RG, H2RG, and H4RG Noise Generator
• Cosmic Ray Rejection and Readout Efficiency for Large-Area Arrays
• deepCR: Cosmic Ray Rejection with Deep Learning
• Validation of Up-the-Ramp Sampling with Cosmic Ray Rejection on IR Detectors
• Optimal Cosmic-Ray Detection for Nondestructive Read Ramps
• Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp
• Cleaning Images with Gaussian Process Regression

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• Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp
• A Classic Nonlinearity Correction Algorithm for Detectors Read Out Up-The-Ramp