Francois Lanusse
with Rachel Mandelbaum
Blending Task Force Meeting
7 Jan. 2019
Input
Reconstruction
Input
Reconstruction
Low KL divergence
High KL divergence
Illustration from Engel et al. 2018
flux_radius [normalised]
code distribution
for real images
code distribution
learned by MAF
Parametric
MAF-VAE
COSMOS
Residuals
M and D statistics from Freeman et al. 2014
$ pip install --user galsim-hub
import galsim
import galsim_hub
from astropy.table import Table
model = galsim_hub.GenerativeGalaxyModel('hub:cosmos_size_mag')
# Defines the input conditions
cat = Table([[5., 10. ,20.],
[24., 24., 24.]],
names=['flux_radius', 'mag_auto'])
# Sample light profiles for these parameters
ims = model.sample(cat)
# Convolve by PSF
ims = [galsim.Convolve(im, psf) for im in ims]
modules:
- galsim_hub
psf :
type : Gaussian
sigma : 0.06 # arcsec
gal :
type : GenerativeModelGalaxy
flux_radius : { type : Random , min : 5, max : 10 }
mag_auto : { type : Random , min : 24., max : 25. }
image :
type : Tiled
nx_tiles : 10
ny_tiles : 10
stamp_size : 64 # pixels
pixel_scale : 0.03 # arcsec / pixel
noise :
type : COSMOS
output :
dir : output_yaml
file_name : demo14.fits
input :
generative_model :
file_name : 'hub:cosmos_size_mag'
Python Interface
Yaml driver
$ galsim demo.yaml
Similar scheme would allow us to draw galaxies image for DC2/DC3
Parametric:
Generative model: