Data-driven galaxy morphology models for image simulations
Francois Lanusse
with Rachel Mandelbaum
Blending Task Force Meeting
7 Jan. 2019
A few words about Generative Models
Variational Auto-Encoder
- Trained to reproduce the input image while trying to match the latent representation to a given prior
\log p(x) \geq E_{z \sim q_{\varphi}(. | x)}[\log p_{\bm{\theta}}(x | z)] - \mathbb{D}_{KL}[q_{\varphi}(\bm{z} | x) || p(z)]
Balance between code regularization and image quality
Input
Reconstruction
Input
Reconstruction
\log p(x) \geq E_{z \sim q_{\varphi}(. | x)}[\log p_{\bm{\theta}}(x | z)] - \mathbb{D}_{KL}[q_{\varphi}(\bm{z} | x) || p(z)]
Low KL divergence
High KL divergence
Illustration from Engel et al. 2018
Conditional Masked Autoregressive Flows for sampling from VAE
- MAF is a state-of-the-art ML technique for density estimation using neural network
- We train a VAE with high KL divergence (i.e. low reconstruction loss) and use a MAF to model the effective distribution of the latent space
- Using a conditional MAF, we can adjust the distribution in the latent space to correspond to populations with desired properties
flux_radius [normalised]
code distribution
for real images
code distribution
learned by MAF
Training a Generative Model on COSMOS galaxies
- We use the GalSim COSMOS 25.2 sample as our training set
- We include the PSF as part of the generative model
- Last layer of the model is a convolution by the known PSF
- Generative model will output essentially unconvolved images
- We use a proper log likelihood taking into account the noise correlations
- We first train an unconditional VAE (time consuming) and then train on top of it a conditional MAF sampler (relatively fast)
- Our fiducial model is conditioned on size and magnitude
Some examples
Parametric
MAF-VAE
COSMOS
AutoEncoder Reconstruction
Residuals
Second order moments
Morphological
statistics
M and D statistics from Freeman et al. 2014
The GalSim Interface
Packaging and serving models
- We want a generic interface that can be used to execute any user provided trained generative model
- Tensorflow Hub Library:
- Computational Graph and trained Weights packaged in single .tar.gz archive
- Takes a set of named inputs, returns a tensor
- Package additional meta-information (e.g. pixel size, stamp size)
- Created galsim-hub, a repository of trained deep generative models which can directly be used by installing the galsim-hub GalSim extension:
$ pip install --user galsim-hubimport 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
Interfacing with BlendingToolKit
- BTK uses the WeakLensingDeblending package to draw images
- Weak Lensing Deblending represents galaxies a sums of Sersic profiles (bulge + disk) and potentially AGN
- bulge + disk parameters, flux in each band is provided by extragalactic catalog
- How can we interface with the generative model in this setting?
Blending ToolKit
COSMOS parametric fits
- The COSMOS sample provided with GalSim includes bulge+disk parametric fits for a subset of galaxies
- We can use these parameters to condition the generative model:
- zphot, bulge_hlr, disk_hlr, bulge_q, disk_q, bulge_flux, disk_flux
- zphot, bulge_hlr, disk_hlr, bulge_q, disk_q, bulge_flux, disk_flux
- The image is drawn for the i-band, and flux is rescaled to match the input catalog in each band
Similar scheme would allow us to draw galaxies image for DC2/DC3
Example of running scarlet on
parametric vs generative model
- Blends drawn and processed through Scarlet using Sowmya's tools.
- In most cases results are very similar, but some visible residuals for more extended objects.
- Working on more quantitative results.
Parametric:
Generative model:
Conclusion
- Framework for integrating generative models with GalSim
- Will release models based on COSMOS along with code to train your own models (GAN and VAE)
- These generative models will allow us to include more realistic morphologies for galaxies in future image simulations
- Update of Ravanbakhsh et al. 2017
Data-driven galaxy morphology models
By eiffl
Data-driven galaxy morphology models
Presentation for Blending Task Force meeting, Jan. 7 2019
