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| wiki:astronomy:observational_astronomy:data_reduction_telescope [2023/11/11 20:21] – Roy Prouty | wiki:astronomy:observational_astronomy:data_reduction_telescope [2024/11/04 19:24] (current) – Roy Prouty |
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| ====== Data Reduction I: Removing Detector Effects ====== | ====== Data Reduction I: Reduction to Top-of-Telescope ====== |
| The term reduction refers to the removal/reduction of extra counts collected in light frames. An appropriately run data reduction pipeline accounts for the effects of the optical system, detector, and atmosphere. This page outlines the theory that can be applied to account for only the effects of the optical system and detector. The non-local [[~:..:data_reduction_toa|effects of the atmosphere must be handled separately]]. | The term reduction refers to the removal/reduction of extra counts collected in light frames. An appropriately run data reduction pipeline accounts for the effects of the optical system, detector, and atmosphere. This page outlines the theory that can be applied to account for only the effects of the optical system and detector. In this way, the counts that make it through this reduction step are due to photoelectrons that were incident at the top-of-the-telescope. The non-local effects of the atmosphere must be handled separately. The [[~:..:data_reduction_skybrightness|removal of light pollution]] due to atmospheric scattering as well as the final calibration to [[~:..:data_reduction_toa|top-of-atmosphere]] are necessary next steps.. |
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| === Unwanted Instrument Signals === | === Unwanted Instrument Signals === |
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| ==== Uniformity ==== | ==== Uniformity ==== |
| $\epsilon_{ij}(\lambda)$ is the pixel-by-pixel uniformity or deviation from uniformity. This is measured as a fraction with each pixel carrying a (to computer precision) continuous value in the range $\epsilon_{ij}(\lambda)\in \mathbb{R}_{[0,1]}$. In an ideal system, $\epsilon_{ij}(\lambda)=1~\forall~i,j$. Broadly, this is the pixel-by-pixel sensitivity to incident photons. "Baked-in" is more than the detector quantum efficiency, it "bakes-in" the reduced efficiency due to any filter, any non-uniformity due to hardware or software amplification via gain modulations, as well as any other imperfections in the optical system that impact the ability for the detector to record photons incident on the primary mirror (all of that schmutz on our mirrors!).\\ | $\epsilon_{ij}(\lambda)$ is the pixel-by-pixel uniformity or deviation from uniformity. This is measured as a fraction with each pixel carrying a (to computer precision) continuous value in the range $\epsilon_{ij}(\lambda)\in \mathbb{R}_{[0,1]}$. In an ideal system, $\epsilon_{ij}(\lambda)=1~\forall~i,j$. Broadly, this is the pixel-by-pixel sensitivity to incident photons. "Baked-in" is more than the detector quantum efficiency, it "bakes-in" the reduced efficiency due to any filter, any non-uniformity due to hardware or software amplification via gain modulations, as well as any other imperfections in the optical system that impact the ability for the detector to record photons incident on the primary mirror (all of that schmutz on our mirrors!). Never forget that frames taken to help us understand the uniformity also have dark signal in them!\\ |
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| ==== Source Signal ==== | ==== Source Signal ==== |
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| Uniformity: Also Poissonian distributed (recall from [[wiki:observational_astronomy:calibration_frames:uniformity|Uniformity (FLATs)]] that FLAT frames are basically light frames of a uniformly illuminated field)\\ \\ | Uniformity: Also Poissonian distributed (recall from [[~:..:calibration_frames:uniformity|Uniformity (flats)]] that FLAT frames are basically light frames of a uniformly illuminated field)\\ \\ |
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