Overview
This page attempts to capture at a high level the software and algorithm development necessary to implement the processing of objects detected at the full survey (at the time of a particular data release), including the detection, deblending, and measurement of sources too faint to be detected in any individual visit. The algorithms to be used here are generally poorly understood; we have many options for extending well-understood algorithms for processing single-epoch data to multi-epoch data, and considerable research is needed to find the right balance between computational and scientific performance in doing so. Unfortunately, different algorithmic options may require vastly different parallelization and data flow, so we cannot yet make assertions about even the high-level interfaces and structure of the code. We do, however, have a good understanding of most of the needed low-level algorithms, so our goal should be to implement these as reusable components that will allow us to quickly explore different algorithmic options. This will also require early access to parallelization interfaces, test data, and analysis tools that will be developed outside the DRP algorithms team.
Inputs
- Calibrated Exposures from Visit Processing
- Final relative astrometric calibration
- Final relative photometric calibration
- Moving and transient sources from Image Differencing and MOPS*
- External Catalogs (e.g. Level 3 inputs or known bright stars)
* We don't need Image Differencing outputs to start the Deep Processing (e.g. we can do Image Coaddition first), and there may be some value to doing the DRP Image Differencing at the same time as some parts of the Deep Processing (Deep Background Modeling, in particular).
Stages/Components
In rough order - exact flow is very much TBD.
Image Coaddition
We'll almost certainly need some sort of coadded image to detect faint sources, and do at least preliminary deblending and measurement. We'll use at least most the same code to generate templates for Image Differencing.
Deep Background Modeling
By matching backgrounds between exposures instead of subtracting them from individual exposures, we should be able to do a better job...
Deep Detection
Deep Deblending
Deep Measurement
Deep Aperture Corrections