The Role of Computer Vision in Astronomy

Rob FergusNew York University

Overview

• Virtually all our knowledge about the universe derived from measurements of photons– Usually as images

• Big astronomy project is $50M-200M– But only 1-2% of this on software

• Just discovering techniques from computer vision & machine learning

Astrometry.net

Lang, Hogg, Mierle, Blanton & Roweis, [The Astronomical Journal, Vol. 137, 2010]

• Input: image of sky

• Output: – Absolute position – List of objects

• Geometric hashing(quads of stars)– Lamdan & Wolfson

[ICCV’88]

• Widely used by pros & amateurs

Removing Atmospheric Distortions

Hirsch, Harmeling, Sra & Schölkopf , [Astronomy & Astrophysics 2011]

• Ground-based telescopes look through atmosphere

• Blind (online) estimation of atmospheric distortion and true image

• Far better than “lucky” imaging (current approach)

Hirsch, Sra, Schölkopf & Harmeling, [CVPR 2010]

Exoplanet Imaging

• Want to image planets around other stars

• Need contrast ratio >1010

for Earth-like planets

• Diffraction in telescope– Light from star obscures planet

• Deconvolution problem– Big assistance from optical design

Crepp et al., [Astrophysical Journal, Vol. 729, 2011].

Galaxy / Star Classification

Star vs Galaxy

[Sloan Digital Sky Survey]

Galaxy / Star Classification

• Distinguish stars from galaxies

• SVM-based models– Smith et al. [A & A, Vol. 522, 2010]

• Generative model of galaxies– Lang et al. [In preparation]

Stars

Galaxies

Data Model

Future Directions

• UnifiedBayesian model

• Propagateuncertaintyfrom pixels

• Physics-informedpriors

Funded by NSF CDI

Cosmology

• Bayesian approaches to fitting high-level cosmological models

http://cmml2011.wikispaces.com/

Cosmic Ray Classification

• Raw image from Hubble Space Telescope: