Galaxy clusters are some of the most massive structures in the cosmos, but despite being millions of lightyears across, they can still be hard to spot. Researchers at Lancaster University have turned to artificial intelligence for assistance, developing “Deep-CEE” (Deep Learning for Galaxy Cluster Extraction and Evaluation), a novel deep learning technique to speed up the process of finding them. Matthew Chan, a PhD student at Lancaster University, is presenting this work at the Royal Astronomical Society’s National Astronomy meeting on 4 July at 3:45pm in the Machine Learning in Astrophysics session.
Most galaxies in the universe live in low-density environments known as "the field", or in small groups, like the one that contains our Milky Way and Andromeda. Galaxy clusters are rarer, but they represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy.
During 1950s the pioneer of galaxy cluster-finding, astronomer George Abell, spent many years searching for galaxy clusters by eye, using a magnifying lens and photographic plates to locate them. Abell manually analysed around 2,000 photographic plates, looking for visual signatures the of galaxy clusters, and detailing the astronomical coordinates of the dense regions of galaxies. His work resulted in the ‘Abell catalogue’ of galaxy clusters found in the northern hemisphere.
Deep-CEE builds on Abell’s approach for identifying galaxy clusters but replaces the astronomer with an AI model that has been trained to “look” at colour images and identify galaxy clusters. It is a state-of-the-art model based on neural networks, which are designed to mimic the way a human brain learns to recognise objects by activating specific neurons when visualizing distinctive patterns and colours.
Chan trained the AI by repeatedly showing it examples of known, labelled, objects in images until the algorithm is able to learn to associate objects on its own. Then ran a pilot study to test the algorithm’s ability to identify and classify galaxy clusters in images that contain many other astronomical objects.
Royal Astronomical Society
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Royal Astronomical Society
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Kate Arkless Gray
Royal Astronomical Society
Dr John Stott
Media representatives are cordially invited to attend the meeting and can register at no cost. Press room facilities will be available in Faraday Seminar Room 2 for the duration of the conference – from 0900 BST on Monday 1 July to 1500 BST on Thursday 4 July. For free registration for the meeting, please contact the press team via firstname.lastname@example.org
Scientists at the conference are also available for interview via both a radio studio and TV studio at Lancaster University; the Globelynx TV network offers broadcast quality HD video to the world’s leading broadcasters and an ISDN line for interviews in a radio booth.
Please book these via the Lancaster University Press Office on +44(0)1524 592612.
Notes for editors
Chan will present the findings of his paper “Fishing for galaxy clusters with “Deep-CEE” neural nets” on 4 July at 3:45pm in the ‘Machine Learning in Astrophysics’ session. (Chan and Stott 2019) which has been submitted to MNRAS and can be found on the Arxiv.
The RAS National Astronomy Meeting (NAM 2019) is taking place at the University of Lancaster from 30 June to 4 July 2019. It includes the annual meetings of the UK Solar Physics (UKSP) and Magnetosphere Ionosphere Solar-Terrestrial (MIST) groups. The conference is principally sponsored by the Royal Astronomical Society (RAS), the Science and Technology Facilities Council (STFC) and Lancaster University. Full details are visible on the dedicated conference website. Access credentials for embargoed press materials are available on request from the NAM press team via email@example.com
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