Artificial Intelligence meets Data Driven Astrophysics Kevin - PowerPoint PPT Presentation

Artificial Intelligence meets Data Driven Astrophysics 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?

Large Synoptic Sky Telescope early 2020s, 20 TB/night

Square Kilometer Array mid 2020s, 160 TB/second

how can machine learning/ artificial intelligence help us understand the universe? do the simplest thing that works!

GalaxyGAN: de-noising and feature reconstruction PSFGAN: point source subtraction Generative models: data-driven exploration

GalaxyGAN: de-noising and feature reconstruction PSFGAN: point source subtraction Generative models: data-driven exploration

resolving power of a telescope R ~ λ /D λ - wavelength at which you observe D - diameter of your telescope aperture

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 Schawinski+17

low-resolution radio data high resolution ground truth GAN prediction NVSS survey FIRST survey Semester project Nina Glaser Glaser et al. in prep.

GalaxyGAN: de-noising and feature reconstruction PSFGAN: point source subtraction Generative models: data-driven exploration

GalaxyGAN: de-noising and feature reconstruction PSFGAN: point source subtraction Generative models: data-driven exploration

Training Architecture Original Discriminator Preprocessing Original + AGN Recovered Generator Dominik Stark PSFGAN, Stark+ in press

GAN performance vesus SOTA parametric fitting tool GALFIT PSFGAN, Stark+ in press

Less sensitive to PSF changes PSFGAN, Stark+ in press

Less sensitive to training set PSFGAN, Stark+ submitted

GalaxyGAN: de-noising and feature reconstruction PSFGAN: point source subtraction Generative models: data-driven exploration

GalaxyGAN: de-noising and feature reconstruction PSFGAN: point source subtraction Generative models: data-driven exploration

Encoder-decoder structure with a domain adversarial aspect Dennis Turp Fader network, Lample+17

Encoder-decoder structure with a domain adversarial aspect

Hypothesis generation

where we want to where we are get to

where we want to where we are get to

changing SSFR changing bulge-to-disk in latent space in latent space

go to space.ml to try out our projects!