Exploring the universe with AI Kevin Schawinski Institute for Particle Physics and Astrophysics ETH Zurich ETH black hole group @kevinschawinski Grüp Bœgg Negar Politecnic da Zürig
how can machine learning/ artificial intelligence help us understand the universe?
“BIG DATA”
GalaxyGAN: de-noising and feature reconstruction PSFGAN: point source subtraction Generative models: data-driven exploration
generative adversarial network for overcoming limitations in astrophysical images Data Prep. Training of GAN Original Image Original Image (Original Image, Degraded Image) or (Recovered Image, Degraded Image) Discriminator Artificial Degrading Degraded Image Generator Recovered Image Schawinski+17
original degraded GAN recovered deconvolved PSF=2.5”, 10 σ
Training Architecture Original Discriminator Preprocessing Original + AGN Recovered Generator Dominik Stark PSFGAN, Stark+ submitted
PSFGAN, Stark+ submitted
Less sensitive to PSF changes Better at recovering features PSFGAN, Stark+ submitted
original data encoder latent space decoder reconstructed data z Dennis Turp
z 1 z 2 z’ = a × z 1 + b × z 2
z 1 z 2 z’ = a × z 1 + b × z 2
original data encoder latent space decoder reconstructed data z z SSFR age z reconstructed galaxies with reconstructed faces with original face SSFR changed in latent space age changed in latent space original galaxy
changing SSFR changing bulge-to-disk in latent space in latent space
machine learning can help us do better science by better understanding the data we have, and will get in the future go to space.ml to try out our projects!
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