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Analysing galaxy images with artificial intelligence: astronomers teach a machine how to ‘see’

Last Updated on Thursday, 09 July 2015 12:49
Published on Tuesday, 07 July 2015 23:01

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 Hocking original image smallHubble 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. Click for a full-size imagethousands 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
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Ms Anita Heward
Royal Astronomical Society
Mob: +44 (0)7756 034 243
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Dr Sam Lindsay
Royal Astronomical Society
Mob: +44 (0)7957 566 861
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Science contacts

 

Mr Alex Hocking
University of Hertfordshire
Mob: +44 (0)741 3098625
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Dr James Geach
University of Hertfordshire
Mob: +44 (0)7528 513 546
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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 sponsored by the Royal Astronomical Society (RAS) and the Science and Technology Facilities Council (STFC). Follow the conference on Twitter

 

The Royal Astronomical Society (RAS), founded in 1820, encourages and promotes the study of astronomy, solar-system science, geophysics and closely related branches of science. The RAS organises scientific meetings, publishes international research and review journals, recognizes outstanding achievements by the award of medals and prizes, maintains an extensive library, supports education through grants and outreach activities and represents UK astronomy nationally and internationally. Its more than 3800 members (Fellows), a third based overseas, include scientific researchers in universities, observatories and laboratories as well as historians of astronomy and others. Follow the RAS on Twitter

 

The Science and Technology Facilities Council (STFC) is keeping the UK at the forefront of international science and tackling some of the most significant challenges facing society such as meeting our future energy needs, monitoring and understanding climate change, and global security. The Council has a broad science portfolio and works with the academic and industrial communities to share its expertise in materials science, space and ground-based astronomy technologies, laser science, microelectronics, wafer scale manufacturing, particle and nuclear physics, alternative energy production, radio communications and radar. It enables UK researchers to access leading international science facilities for example in the area of astronomy, the European Southern Observatory. Follow STFC on Twitter