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Blending in LSST Data Products Jim Bosch, DM DRP Scientist / - - PowerPoint PPT Presentation
Blending in LSST Data Products Jim Bosch, DM DRP Scientist / - - PowerPoint PPT Presentation
Blending in LSST Data Products Jim Bosch, DM DRP Scientist / Princeton Blending Families Two Footprints: 2 above-threshold regions with peaks. 4 One isolated object ( 1 ). 3 One Parent ( 2 ): 5 blends measured with no deblending. Three
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Blending Families
1 2 3 4 1 2 3 4
id parent deblend_nChild 1 2 3 3 2 4 2 5 2
this is a tree: represented as a table:
5 5
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Useful Subsets
1 2 3 4
The full table is not a useful subset: (3, 4, 5) and 2 are mutually exclusive.
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
1 2 3 4 5
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Useful Subsets
1 2 3 4
Usually you want both isolated and deblended objects:
deblend_nchild = 0
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
1 2 3 4 5
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Useful Subsets
1 2 3 4
If you're interested in really bright objects (bright enough to ignore their neighbors), and you don't trust the deblender:
parent = 0
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
1 2 3 4 5
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Useful Subsets
1 2 3 4
If you don't trust the deblender, and don't mind an incomplete sample:
deblend_nchild = 0 AND parent = 0
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
1 2 3 4 5
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Footprints and HeavyFootprints
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
5
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Footprints and HeavyFootprints
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
5
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Footprints and HeavyFootprints
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
5
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Footprints and HeavyFootprints
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
5
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Footprints and HeavyFootprints
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id parent deblend_nChild 1 2 3 3 2 4 2 5 2
5
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Extending the Tree
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id parent deblend_nChild 1 2 3 3 2 2 4 2 5 2 6 3 7 3
5 6 7
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Association Flags
When merging detections from different bands, we set flags to indicate where the Object came from: merge_footprint_<band>: there is a Footprint in <band> that overlaps the parent Footprint. merge_peak_<band>: there is a peak in <band> near the peak that spawned a child object.
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Deblender Flags
These are algorithm-dependent, and they may look completely different in DR1 (or even a few months from now, if we've switched to Scarlet): deblend_deblendedAsPsf: Deblender thought this source looked like a PSF deblend_tooManyPeaks: source had too many peaks; only the brightest were included deblend_parentTooBig: Parent footprint covered too many pixels deblend_masked: Parent footprint was predominantly masked deblend_skipped: Deblender skipped this source deblend_hasStrayFlux: This source was assigned some stray flux
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Blendedness
We want a metric with the following properties:
- zero for isolated objects;
- approaches unity for when an object is much fainter than its
neighbor(s);
- related to how much the photometry of the primary object
could have been affected by its neighbors;
- can be derived from real data (doesn't require ground truth).
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Blendedness
If we knew the true child and parent profiles, we would use:
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Blendedness
For real data (and real deblends), we use:
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Blendedness
Using the deblended Child and original-data Parent is noisy.
- That's why we use the Gaussian model instead of the Child itself
in some places:
- We also compute a variant that uses a de-biased absolute value
- f the Parent and Child:
See HSC Pipeline Paper (Bosch et al 2018) for more details.
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Summary
- The outputs of deblending are a tree, even though we flatten
that tree into a table.
- The best way to learn what happened in deblending is to look at
the actual results - the HeavyFootprints.
- There will be lots of deblender and association flags. But the
flags we have now will probably change with the algorithms.
- Blendedness is one useful estimator for how how affected an
- bject was by blending; there may be others.