{"title":"上斜坡读出探测器的最佳拟合和去锯齿化","authors":"Timothy D. Brandt","doi":"10.1088/1538-3873/ad38d9","DOIUrl":null,"url":null,"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 χ2 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 × 4096 detector in ≈8 s 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 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.","PeriodicalId":20820,"journal":{"name":"Publications of the Astronomical Society of the Pacific","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp\",\"authors\":\"Timothy D. Brandt\",\"doi\":\"10.1088/1538-3873/ad38d9\",\"DOIUrl\":null,\"url\":null,\"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 χ2 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 × 4096 detector in ≈8 s 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 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.\",\"PeriodicalId\":20820,\"journal\":{\"name\":\"Publications of the Astronomical Society of the Pacific\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Publications of the Astronomical Society of the Pacific\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1088/1538-3873/ad38d9\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Publications of the Astronomical Society of the Pacific","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1538-3873/ad38d9","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
Optimal Fitting and Debiasing for Detectors Read Out Up-the-Ramp
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 χ2 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 × 4096 detector in ≈8 s 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 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.
期刊介绍:
The Publications of the Astronomical Society of the Pacific (PASP), the technical journal of the Astronomical Society of the Pacific (ASP), has been published regularly since 1889, and is an integral part of the ASP''s mission to advance the science of astronomy and disseminate astronomical information. The journal provides an outlet for astronomical results of a scientific nature and serves to keep readers in touch with current astronomical research. It contains refereed research and instrumentation articles, invited and contributed reviews, tutorials, and dissertation summaries.