Detections with Celeste #46
Description
Shear Reading 2021-04-08 10:00
Bastien: Celeste
- Not detection, but a full generative model.
- Completely different from sextractor & tractor
- build a model from the parameter disribution space
- more like a model fitting than a detection procedure
- choose 1 reference band and colors to find other filters
- testing on SDSS-like generated images
- some parameters are fixed in advance
- each pixel may have contributions from several objects
- brightness: gamma function distributed
- color distribution: multivariate gaussian
- mixture component is a categorical distribution
- galaxy models: bulge + disk
- mu_s position/detection
- priors from the position, from other surveys
- proceed with fitting
- when no positions available: convolve wihth matched filters to increase snr. find pixels value exceeds local + upper bound of noise.
- Test: Stripe82 using Photo (Lupton) as prior.
- positions are better by ~1-% (N=654)
Axel:
- are they detecting galaxies? (Table 2 of celeste paper)
- why are they detecting more galaxies? Blends?
Andre:
- The input catalogue is just a prior.
Bastien:
- Then the fit is optimized.
Andre:
- Starnet partitions images to control positions/parameter space
Bastien:
- part of LSST DESC, same people
- generative models in starnet represent objects better.
Andre:
- do they quote size/storage demands?
Bastien:
- celeste takes 5 min in a 4 megapixel image with hundreds of obj.
- in starnet tiling, in celeste, are there limits of objects contributing to a pixel?
Axel:
- at least one problem: fit the number of obj at some point: you have to fix the # of obj at some point.
- not see the point, you'll have to fix # at some point.
Andre:
- you can integrate on all distributions (but it ridiculous).
- could this give us a mitigation for the starlink problem?
Bastien:
- Celeste was kind of a first step, starnet came after.
Axel:
- do they assume models for galaxies:
Bastien:
- yes, bulge plus disk
Axel:
- is this used for the detection
Bastien:
- as you fit everything at once, yes, you can't separate it
Axel:
- if you have too much variables, things explode, its computationally difficult, and gains of it are low.
Bastien:
- in the end they do fix a lot of things.
Axel:
- interesting for metadetection. there are artifacts on images that are difficult to treat.
- for blends is a really good way
Bastien:
- in non-blends maybe this wont get better positions, but in blends, maybe yes.
- starnet uses the same tests on coadds
Andre:
- first & second pass
Axel:
- do they need a prior on the psf?
Bastien:
- they use a mix of Gaussian fitted to known stars.