Deep Vacuum Cleaner

Deconvolving radio astronomy images using a convolution network.

Transcript

1. Deep Vacuum Cleaner Gijs Molenaar
2. Deep Learning, why? • Deep learning seems to perform well on image to image tasks • Using convolution network for learning inverse of convolution • Can learn any function given enough nodes in hidden layer • Recursive networks are Turing complete • Interesting to get experience with
3. The architecture • No idea! • Auto encoder? Recursive Neural Machines? LSTM?
4. Pix2pix
5. generative adversarial U- shape auto encoder network • Actually 2 networks • Generator • Discriminator (used during training)
6. The generator
7. The discriminator
8. Putting it together
9. Training pipeline • Spiel • CWL based https://github.com/gijzelaerr/spiel/
11. Simulation Job
13. In short • Works on Linux, OS X (Containers) • Implicit parallelisation • Support for various schedulers (Slurm, Mesos) • Open Standard
14. Training data • Deep learning usually needs a lot of data • Point sources only • 256x256 fits • 2000 Meerkat16 datasets • We can flip and rotate those! Boom, more data. • Randomised: sky position, synthesis time, channel width, fluxes
15. Pre/post processing • Scale fluxes to 1 / -1 • Rescale output back
19. Test set RMS residuals
20. What (doesn’t) works • All bright sources found • PSF independent • Fluxes are wrong • Network is good at generating images that ‘look like’ what we want
21. Experiment PSF • Add PSF to network as channel • Network converges faster • Probably only helps in center of image
22. Experiment Vis2img • Statistically good looking nonsense
23. Experiment vis2vis • training… • Error based image space
24. Time performance • Deconvolving deep2 image • 1089 tiles • 30 seconds total • 10 seconds ‘warming up’ • 20 ms per tile • Exported model is about 100 megabytes
25. Applications • Quick and dirty cleaning? • Quickly get statistical estimates?
26. Future work • Better train data • Try different architecture • Infrastructure is there • Collaborate with Deep Learning experts • Recurrent Inference Machines for Solving Inverse function ill- posed problems • https://arxiv.org/abs/ 1706.04008
27. Fin