Machine Learning in Large Radio Astronomy Surveys

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1 Machine Learning in Large Radio Astronomy Surveys (How to do Science with Petabytes) Ray Norris, Western Sydney University & CSIRO Astronomy & Space Science,

2 ASKAP: Australian Square Kilometre Array Pathfinder $185m telescope built by CSIRO, approaching completion Mission: to solve fundamental problems in astrophysics EMU = Evolutionary Map of the Universe

3 PAFs -> Big Data Data Rate to correlator = 100 Tbit/s = 3000 Blu-ray disks/second = 62km tall stack of disks per day = world internet bandwidth in June 2012 Processed data volume = 70 PB/yr (only store 4 PB/yr)

4 EMU: Evolutionary Map of the Universe PI Ray Norris Will survey the whole sky for radio continuum Will discover ~ 70 million galaxies, compared to 2.5 million currently known Will revolutionize our view of the Universe Will revolutionize the way we do astronomy large-n astronomy

5 ASKAP Radio Continuum survey: EMU = 70 million NVSS=1.8 million current total=2.5 million From Norris, 2017, Nature Astronomy, 1,

6 EMU Team: ~300 scientists in 21 countries Key project Title Project Leader KP1. EMU Value-Added Catalogue Nick Seymour (Curtin) KP2. Characterising the Radio Sky Ian Heywood (Oxford) KP3. EMU Cosmology David Parkinson (KASA, Korea) KP4. Cosmic Web Shea Brown (Iowa) KP5. Clusters of Galaxies Melanie Johnston-Hollitt (NZ) KP6. cosmic star formation history Andrew Hopkins (AAO) KP7. Evolution of radio-loud AGN Anna Kapinska (UWA) KP8. Radio AGN in the EoR Jose Afonso (Lisbon) KP9. Radio-quiet AGN Isabella Prandoni (Bologna) KP10. Binary super-massive black holes Roger Deane (Cape Town) KP11. Local Universe Josh Marvil (NRAO) KP12. The Galactic Plane Roland Kothes (Canada) KP13. SCORPIO: Cataloguing the Radio Stars in our Galaxy Grazia Umana (Catania) KP14. WTF: Discovering the Unexpected Ray Norris (CSIRO/WSU) KP15 The Magellanic Clouds Miroslav Filipovic (WSU)

7 Bad news: even with 300+ scientists, we cannot analyse data in traditional ways Integrated Sachs-Wolfe effect can measure Dark Energy Good news: with 70 million galaxies, we can extract the science from the data in innovative ways -> large-n astronomy EMU can cross-correlate galaxy positions against the cosmic microwave background

8 E.g: Cosmic magnification enables us to measure whether gravity still obeys General Relativity at large distances Large-n approach: EMU can cross-correlate foreground galaxies and background galaxies.

9 But to do these tests we need to know (roughly) the distances (redshifts) of the galaxies Traditional approach: measure redshifts with a spectrometer on a large optical telescope Large-n approach: Use machine learning on all observational features to estimate the statistical redshift

10 ML-determined redshift Currently comparing machine-learning techniques for redshift measurement. Neural net Random Forest (NA) Random Forest (JHU) knn Fit Residual Spectroscopic redshift From Norris et al. 2018, submitted to PASP

11 There s nothing as useless as a radio source (Jim Condon, 2011) Data from Jordan Collier PhD thesis

12 Source classification & Cross-identification Problem: radio sources can consist of several components

13 Cross-identification Radio image (contours) overlaid on infrared image (greyscale) Radio image

14 Radio Galaxy Zoo Radio Galaxy Zoo Project started in 2010 to solve the EMU cross-id problem Initially used ATLAS but then added FIRST Launched in December 2013 >12,000 citizen scientists >2,000,000 classifications >120,000 galaxies classified

15 i Radio Galaxy Zoo (over 2 million identifications by citizen scientists) Double radio sources have an infrared galaxy between the radio components (image courtesy of Mathew Alger and Radio Galaxy Zoo) Single radio sources have an infrared galaxy at the same position as the radio components Radio contours on infrared grey-scale

16 Current Radio Source cross-id projects Expert manual cross-id for training/test (lead: Jesse Swan,U. Tas) Radio Galaxy Zoo (lead: Ivy Wong, UWA) Bayesian (lead: Dongwei Fan, NO/CAS, & Tamas Budavari,JHU) Convol. Neural Net (several groups) Self-organized maps (Tim Galvin, CSIRO) Self-organized maps with auto-encoders (Nic Ralph, WSU) Image complexity (lead: Gary Segal, UQ) (Gold standard reliability: NVSS 90%, ATLAS/EMU 99%)

17 A Bayesian approach: Pulsars for Public & Pupils Robert Hollow

18 Machine learning in astronomy collaboration Participants from WSU, CSIRO (CASS, Data61, IM&T) ANU, Sydney Uni, UQ, U.Iowa, U. Minnesota, U.Calgary, etc. Regular informal zoom research meetings take place every 2 nd Thursday at ADST, AWST, UTC on Provide data sets and training sets for experimenting E.g. ATLAS DR3, synthetic data sets, etc Other resources see We welcome all interested to join our discussion meetings.

19 the traditional owners of the ASKAP site Western Australia See our newsletter on