{"action":"created","comment":{"url":"https://api.github.com/repos/lsst/LDM-151/pulls/comments/66787212","id":66787212,"diff_hunk":"@@ -404,22 +398,160 @@ \\subsubsection{FinalJointCal}\n \\subsection{Coaddition and Difference Imaging}\n \\label{sec:drp_coaddition_and_diffim}\n \n-\\begin{note}[Coaddition, DiffIm Diagram]\n-Extract Coaddition and DiffIm pipelines from ``DRP Top-Level Overview'' on confluence and expand detail to show data flow and ordering of ``Task/Process'' boxes.\n-\\end{note}\n+The next group of pipelines in a Data Release Production consist of image coaddition and image differencing, which we use to separate the static sky from the dynamic sky in terms of both astrophysical quantities and observational quantities.  This group also includes an iteration between pipelines that combine images and pipelines that subtract the combined images from each exposure.  At each differencing step, we better characterize the features that are unique to a single epoch (whether artifacts, background features, or astrophysical sources); we use these characterizations to ensure the next round of coadds include only features that are common to all epochs.\n+\n+\\begin{figure}\n+\\centering\n+\\includegraphics[width=\\textwidth]{figures/drp_coaddition_and_diffim.png}\n+\\caption{\n+  Data flow diagram for the Data Release Production coaddition and difference imaging pipelines.  Processing proceeds roughly counterclockwise, starting from the upper right with pipelines described in Section~\\ref{sec:drp_imchar_and_jointcal}.  Each update to a component of the central CalExp dataset can in theory trigger another iteration of a previous loop, but in practice we will ``unroll'' these loops before production begins, yielding an acyclic graph with a series of incrementally updated CalExp datasets.  The nature of this unrolling and the number of iterations will be determined by future algorithmic research.  Numbered steps above are described more fully in the text.\n+  \\label{fig:drp_coaddition_and_diffim}\n+}\n+\\end{figure}\n+\n+The processing flow in this pipeline group again centers around incremental updates to the CaExp dataset, which are limited here to its Background and Mask component (the Image component is also updated, but only to subract the updated background).  It will also return to the previous pipeline group described in Section~\\ref{sec:drp_imchar_and_jointcal} to update other CalExp components.  The data flow between pipelines is shown in Figure~\\ref{fig:drp_coaddition_and_diffim}, with the numbered steps are described further below:\n+\\begin{enumerate} % note that the figure numbers aren't automatically linked\n+\\item The first version of the CalExp dataset is produced by running the \\hyperref[sec:drpBootstrapImChar]{BootstrapImChar}, \\hyperref[sec:drpStandardJointCal]{StandardJointCal}, and \\hyperref[sec:drpRefineImChar]{RefineImChar} pipelines, as described in Section~\\ref{sec:drp_imchar_and_jointcal}.\n+\\item We generate an updated Background and Mask via the \\hyperref[sec:drpWarpAndPsfMatch]{WarpAndPsfMatch} and \\hyperref[sec:drpBackgroundMatchAndReject]{BackgroundMatchAndReject} pipelines.  This produces the final CalExp Background and Image, and possibly the final Mask.\n+\\item If the CalExp Mask has been finalized, we run the \\hyperref[sec:drpFinalImChar]{FinalImChar} and \\hyperref[sec:drpFinalJointCal]{FinalJointCal} pipelines.  These produce the final PSF, WCS, and PhotoCal.  If the Mask has not been finalized, we execute at least one iteration of the next step before this one.\n+\\item We run the \\hyperref[sec:drpWarpTemplates]{WarpTemplates}, \\hyperref[sec:drpCoaddTemplates]{CoaddTemplates}, and \\hyperref[sec:drpDiffIm]{DiffIm} pipelines to generate the DIASource and DIAExp datasets.  We may then be able to generate better CalExp Masks than we can obtain from \\hyperref[sec:drpBackgroundMatchAndReject]{BackgroundMatchAndReject} by comparing the DIAExp masks across visits in the \\hyperref[sec:drpUpdateMasks]{UpdateMasks} pipeline.\n+\\item After all CalExp components have been finalized, we run the \\hyperref[sec:drpWarpRemaining]{WarpRemaining} and \\hyperref[sec:drpCoaddRemaining]{CoaddRemaining} to build additional coadd data products.\n+\\end{enumerate}\n+The baseline ordering of these steps is thus \\{1,2,3,4,5\\}, but \\{1,2,4,3,4,5\\} is perhaps just as likely, and we may ultimately require an ordering that repeats steps 2 or 3.  Final decisions on the ordering and number of iteration will require testing with mature pipelines and a deep dataset taken with a realistic cadence; it is possible the configuration could even change between data releases as the survey increases in depth.  Fortunately, this reconfiguring should not require significant new algorithm development.\n+\n+This pipeline group is responsible for producing the following final data products:\n+\\begin{description}\n+\\item[CalExp.\\{Image,Background,Mask\\}]  See above.\n+\\item[DIAExp] A CCD-level \\hyperref[sec:spImagesExposure]{Exposure} that is the difference between the CalExp and a template coadd, in the coordinate system and with the PSF of the CalExp.\n+\\item[DIASource] A \\hyperref[sec:spTablesSource]{SourceCatalog} containing sources detected and measured on the DIAExp images.\n+\\item[ConstantPSFCoadd] A coadd data product (\\hyperref[sec:spImagesExposure]{Exposure} or subclass thereof) with a constant, predefined PSF.\n+\\item[DeepCoadd] A coadd data product built to optimize depth at the possible expense of seeing.\n+\\item[BestSeeingCoadd] A coadd data product built to optimize image quality at the possible expense of depth.  Depending on the algorithm used, this may be the same as DeepCoadd.\n+\\item[ShortPeriodCoadd] A coadd data product built from exposures in a short range of epochs, such as a year, rather than the full survey.\n+\\item[LikelihoodCoadd] A coadd formed by correlating each image with its own PSF before combining them, used for detection and possibly building other coadds.\n+\\item[ShortPeriodLikelihoodCoad] Short-period likelihood coadds will also be built.\n+\\item[TemplateCoadd] A coadd data product used for difference imaging in both DRP and AP, \\emph{or} some other tract/patch/filter data product from which the template for a given airmass can be derived.\n+\\end{description}\n+\n+The nature of these coadd data products depends critically on whether we are able to develop efficient algorithms for optimal coaddition, and whether these coadds are suitable for difference imaging.  These algorithms are mathematically well-defined but computationally difficult; see \\href{http://dmtn-015.lsst.io/en/latest/}{DMTN-15} for more information.  We will refer to the coadds produced by these algorithms as ``decorrelated coadds''; a variant with constant PSF (``constant-PSF partially decorrelatd coadd'') is also possible.  This choice is also mixed with the question of how we will correct for differential chromatic refraction in difference imaging; some algorithms for DCR correction involve templates that are the result of inference on input exposures rather than coaddition.  There are five main scenarios for our use of decorrelated coadds:\n+\\begin{description}\n+  \\item[A] \\label{option:decorrelated-everything} We use decorrelated coadds for all final coadd products.  DeepCoadd and ShortPeriodCoadd will be standard decorrelated coadds with a spatially-varying PSF, and ConstantPSFCoadd and TemplateCoadd will be constant-PSF partially-decorrelated coadds.  The BestSeeingCoadd data product will be dropped, as it will be redundant with DeepCoadd.  This will make coadds more expensive and complex to build, and require more algorithm development for coaddition, but will improve coadd-based measurements, make it easier to warm-start multi-epoch measurements.  Difference imaging may be easier, and more visits may be usable as inputs to templates due to softened or eliminated seeing cut.\n+  \\item[B] \\label{option:decorrelated-meas-coadds} We use decorrelated coadds for all coadds but TemplateCoadd.  Measurement is still improved, and the additional computational cost of coaddition is limited to a single pipeline that is not run iteratively.  Difference imaging may be harder, and the number of visits eligible for inclusion in templates may be reduced.  In this scenario, we still have two options for building templates:\n+  \\begin{description}\n+    \\item[B1] \\label{option:psf-matched-templates-b} Templates will be built as PSF-matched coadds, or a product of PSF-matched coadds.\n+    \\item[B2] \\label{option:inferred-templates-b}Templates are the result of inference on resampled exposures with no PSF-matching.\n+  \\end{description}\n+  \\item[C] \\label{option:no-decorrelated-coadds} We do not use decorrelated coadds at all.  DeepCoadd, BestSeeingCoadd, and ShortPeriodCoadd will be direct coadds, and ConstantPSFCoadd will be a PSF-matched coadd.  Coaddition will be simpler and faster, but downstream algorithms may require more sophistication, coadd measurements may be lower quality, and multi-epoch measurements may be more difficult to optimize.  Here we again have the same two options for templates as option \\ref{option:decorrelated-meas-coadds}:\n+  \\begin{description}\n+    \\item[C1] \\label{option:psf-matched-templates-c} Templates will be built as PSF-matched coadds, or a product of PSF-matched coadds.\n+    \\item[C2] \\label{option:inferred-templates-c} Templates are the result of inference on resampled exposures with no PSF-matching.\n+  \\end{description}\n+\\end{description}\n+It is also possible to combine multiple scenarios across different bands.  In particular, we may not need special templates to handle DCR in redder bands, so we may select a simpler approach to dealing with them in those bands.  The final selection between these options will require experiments on LSST data or precursor data with similar DCR and seeing, though decorrelated coaddition algorithms and some approaches to DCR correction may be ruled out earlier if preliminary algorithm development does not go well.\n+\n+Further differences in the pipelines themselves due to the presence or absence of decorrelated coadds will be described in the sections below.\n \n \\subsubsection{WarpAndPsfMatch}\n \\label{sec:drpWarpAndPsfMatch}\n+\n+This pipeline resamples and then PSF-matches CalExp images from a visit into a single patch-level image with a constant PSF.  The resampling and PSF-matching can probably be accomplished separately by delegating to the \\hyperref[sec:acWarping][Image Warping] and \\hyperref[sec:acPSFHomogenization]{PSF Homogenization} algorithmic components, respectively.  These operations can also be performed in the opposite order if the matched-to PSF is first transformed to the CalExp coordinate systems (so subsequent resampling yields a constant PSF in the coadd coordinate system).  Doing PSF-matching first may be necessary (or at least easier to implement) for undersampled 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