This document discusses the use of machine learning techniques for analyzing astronomical and earth observation data. It notes that large sky survey projects generate huge amounts of data that exceeds our ability to analyze using traditional methods. Machine learning can help process and extract insights from this big data. Specifically, the document discusses how convolutional neural networks have achieved 98% accuracy in classifying galaxy images from sky surveys. It also provides examples of applying machine learning to tasks like terrain classification from earth observation satellite imagery.

1. Deep Learning in Deep Space
Applica(ons of Machine Learning
on Astronomical Data
Dr. Brian Mac Namee
brian.macnamee@ucd.ie
@brianmacnamee

2. Tycho Brahe (1546 –1601)

3. Big sky survey projects are amongst the biuggest data sources available
Sloan Digital Sky Survey (www.sdss.org)

4. Sloan Digital Sky Survey (www.sdss.org)

5. Sloan Digital Sky Survey (www.sdss.org)

6. The SDSS telescopes
collect over 200GB of
data every night
Over 200 million
galaxies have been
photographed
Data has become a
huge boPle neck in
modern astronomy
Future telescopes will
collect even more
data
Sloan Digital Sky Survey (www.sdss.org)

7. Data
Collec(on
Data
Processing
Science

8. Data
Collec(on
Data
Processing
Science
Machine
Learning
* Connec(on from data to Brahe to Newton
stolen from Prof. James Gleeson (UL)

9. Data
Collec(on
Data
Processing
Science
Machine
Learning
* Connec(on from data to Brahe to Newton
stolen from Prof. James Gleeson (UL)
(Machine Learning)
Correla(on
or
Causa(on
(Sta(s(cs)

10. Data
Collec(on
Data
Processing
Science
Machine
Learning
* Connec(on from data to Brahe to Newton
stolen from Prof. James Gleeson (UL)
(Machine Learning)
Correla(on
or
Causa(on
(Sta(s(cs)

12. Ellipse
Le0 Spiral
Right Spiral
Fundamentals of Machine Learning
for Predic(ve Data Analy(cs
John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
MIT Press
www.machinelearningbook.com

13. Support Vector Machine Performance
Fundamentals of Machine Learning for Predic(ve Data Analy(cs
John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
MIT Press
www.machinelearningbook.com
Overall accuracy:
~88%

14. Support Vector Machine Performance
Fundamentals of Machine Learning for Predic(ve Data Analy(cs
John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
MIT Press
www.machinelearningbook.com
Convolu(onal Neural Network Performance
Overall accuracy:
~88%
Overall accuracy:
~98%
"Rota(on-invariant convolu(onal neural networks for galaxy morphology predic(on.", Dieleman, Sander, Kyle W.
WilleP, and Joni Dambre. Monthly no)ces of the royal astronomical society 450, no. 2 (2015): 1441-1459.

15. Galaxy Catalogue (hPp://www.zsolt-frei.net/galaxy_catalog.html)
Data is the Fuel of Machine Learning

16. Galaxy Catalogue (hPp://www.zsolt-frei.net/galaxy_catalog.html)
Data is the Fuel of Machine Learning
Labelled data
is the real key

17. Galaxy Zoo (www.galaxyzoo.org)
Crowdsourcing Labelled Data

18. Galaxy Zoo (www.galaxyzoo.org)
Crowdsourcing Labelled Data
Almost 1,000,000 galaxies
labelled
Over 150,000 users
Millions of individual
labels

19. Other uses of machine learning in astronomy:
– Object classifica(on (stars, galaxies, quasars, ...)
– Es(ma(ng redshil
– Es(ma(ng other parameters of sky objects
– Forecas(ng solar flare ac(vity
Kremer, Jan, Kristoffer Stensbo-Smidt, Fabian Gieseke, Kim Steenstrup Pedersen, and Chris(an Igel. "Big Universe, Big Data: Machine Learning
and Image Analysis for Astronomy." IEEE Intelligent Systems 32, no. 2 (2017): 16-22.
Ball, Nicholas M., and Robert J. Brunner. "Data mining and machine learning in astronomy." Interna)onal Journal of Modern Physics D 19, no. 07
(2010): 1049-1106.

20. European Space Agency (www.galaxyzoo.org)
Earth Observa(on

21. Earth Observa(on Terrain
classifica(on is a
standard task
Convolu(onal
neural networks
are leading
approach for this
task
Accuracies of
>90% possible
Classifica(on and segmenta(on of
satellite orthoimagery using
convolu(onal neural networks,
Längkvist, Mar(n, Andrey Kiselev,
Marjan Alirezaie, and Amy Lousi,
Remote Sensing 8, no. 4 (2016): 329.

22. Other uses of machine learning in earth
observa(on:
– Offers poten(al to use mul(-spectral images
– Terrain classifica(on
– Target recogni(on
– Seman(c feature extrac(on
– Scene understanding

23. Thank you
Ques<ons?
Dr. Brian Mac Namee
brian.macnamee@ucd.ie
@brianmacnamee
www.bigskyearth.eu
www.machinelearningbook.com