Credit: J. Geach / A. Hocking. Click for a full size image
Image showing the MACS0416.1-2403 cluster, highlighting parts of the image that the algorithm has identified as ‘star-forming’ galaxies.
Credit: NASA / ESA / J. Geach / A. Hocking. Click here for a full size image
Image showing the MACS0416.1-2403 cluster, highlighting parts of the image that the algorithm has identified as ‘elliptical’ galaxies.
Credit: NASA / ESA / J. Geach / A. Hocking. Click here for a full size image
A team of astronomers and computer scientists at the University of Hertfordshire have taught a machine to 'see' astronomical images. The technique, which uses a form of artificial intelligence called unsupervised machine learning, allows galaxies to be automatically classified at high speed, something previously done by thousands of human volunteers in projects like Galaxy Zoo. Masters student Alex Hocking led the new work and presented it for the first time in a paper today (July 8) at the National Astronomy Meeting at Venue Cymru, Llandudno, Wales.
The team have demonstrated their algorithm using data from the Hubble Space Telescope ‘Frontier Fields’: exquisite images of distant clusters of galaxies that contain several different types of galaxy.
Mr Hocking, who led the new work, commented: “The important thing
about our algorithm is that we have not told the machine what to look
for in the images, but instead taught it how to 'see'."
His supervisor and fellow team member Dr James Geach
added: “A human looking at these images can intuitively pick out and
instinctively classify different types of object without being given any
additional information. We have taught a machine to do the same thing."
‘Our aim is to deploy this tool on the next generation of giant
imaging surveys where no human, or even group of humans, could closely
inspect every piece of data. But this algorithm has a huge number of
applications far beyond astronomy, and investigating these applications
will be our next step," concludes Geach.
The scientists are now looking for collaborators, making good use of
the technique in applications like medicine, where it could for example
help doctors to spot tumours, and in security, to find suspicious items
in airport scans.
Images and captions
Visualisation of the neural network representing the ‘brain’ of the machine learning algorithm.
The intersections of lines are called nodes, and these represent a map
of the input data. Nodes that are closer to each other represent similar
features within the data. Fainter lines show how the network has
evolved over time as the algorithm processes more data. Credit: J. Geach
/ A. Hocking
A zoom-in of part of the network described above. Credit: J. Geach / A. Hocking
Hubble Space Telescope image of the cluster of galaxies MACS0416.1-2403,
one of the Hubble ‘Frontier Fields’. Bright yellow ‘elliptical’
galaxies can be seen, surrounded by numerous blue spiral and amorphous
(star-forming) galaxies. Gravitational arcs can also be seen. This image
forms the test data that the machine learning algorithm is applied to,
having not previously ‘seen’ the image. Credit: NASA / ESA / J. Geach /
A. Hocking
Image
showing the MACS0416.1-2403 cluster, highlighting parts of the image
that the algorithm has identified as ‘star-forming’ galaxies. Credit: NASA / ESA / J. Geach / A. Hocking
Image
showing the MACS0416.1-2403 cluster, highlighting parts of the image
that the algorithm has identified as ‘elliptical’ galaxies. Credit: NASA / ESA / J. Geach / A. Hocking
Media contacts
Dr Robert Massey
Royal Astronomical Society
Mob: +44 (0)794 124 8035
rm@ras.org.uk
Ms Anita Heward
Royal Astronomical Society
Mob: +44 (0)7756 034 243
anitaheward@btinternet.com
Dr Sam Lindsay
Royal Astronomical Society
Mob: +44 (0)7957 566 861
sl@ras.org.uk
Science contacts
Alex Hocking
University of Hertfordshire
Mob: +44 (0)741 3098625
alexander.hocking@gmail.com
James Geach
University of Hertfordshire
Mob: +44 (0)7528 513 546
j.geach@herts.ac.uk
Further information
The new work appears in “Teaching a machine to see: unsupervised image
segmentation and categorisation using growing neural gas and
hierarchical clustering”, A. Hocking, J. E. Geach, N. Davey & Y.
Sun. The paper has been submitted to Monthly Notices of the Royal Astronomical Society.
Notes for editors
The Royal Astronomical Society National Astronomy Meeting
(NAM 2015) will take place at Venue Cymru in Llandudno, Wales, from 5-9
July. NAM 2015 will be held in conjunction with the annual meetings of
the UK Solar Physics (UKSP) and Magnetosphere Ionosphere
Solar-Terrestrial physics (MIST) groups. The conference is principally
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Technology Facilities Council (STFC). Follow the conference on Twitter
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