A gravitational lens is a distribution of matter (such as a cluster of galaxies) between a distant light source and an observer that is capable of bending the light from the source as the light travels toward the observer, and it is one of the predictions of Albert Einstein’s general theory of relativity.
Earlier this year a machine learning algorithm identified up to 5,000 potential gravitational lenses that could transform our ability to chart the evolution of galaxies since the Big Bang.
Now astronomer Kim-Vy Tran and colleagues have assessed 77 of the lenses using the Keck Observatory in Hawai’i and Very Large Telescope in Chile. She and her international team, including Lisa Kewley of the Center for Astrophysics, confirmed that 68 out of the 77 are strong gravitational lenses spanning vast cosmic distances.
The success rate of 88 percent suggests that the algorithm is reliable and that there could be thousands of new gravitational lenses. To date, gravitational lenses have been hard to find and only about a hundred are routinely used.
Tran’s paper, published recently in the Astronomical Journal, presents spectroscopic confirmation of strong gravitational lenses previously identified using Convolutional Neural Networks, developed by data scientist Colin Jacobs at ASTRO 3D and Swinburne University.
The work is part of the ASTRO 3D Galaxy Evolution with Lenses (AGEL) survey.
“Our spectroscopy allowed us to map a 3D picture of the gravitational lenses to show they are genuine and not merely chance superposition,” says Tran from the ARC Centre of Excellence for All Sky Astrophysics in 3-Dimensions (ASTRO3D) and the University of NSW (UNSW).
“Our goal with AGEL is to spectroscopically confirm around 100 strong gravitational lenses that can be observed from both the Northern and Southern hemispheres throughout the year,” she says.
The paper is the result of a collaboration spanning the globe with researchers from Australia, the United States, the United Kingdom, and Chile.
The work was made possible by the development of the algorithm to look for certain digital signatures.
“This research merges gravitational lensing, an effect predicted by Einstein, with modern machine learning techniques to dramatically increase our discoveries of galaxies in the distant universe,” says Kewley, a study co-author and director of the Center for Astrophysics | Harvard & Smithsonian. “This is the first peek at what will be an avalanche of data of lensed galaxies to help us understand how galaxies like our Milky Way formed and evolved across 13 billion years of cosmic time.”
Gravitational lensing was first identified as a phenomenon by Einstein who predicted that light bends around massive objects in space in the same way that light bends going through a lens.
In doing so, it greatly magnifies images of galaxies that we would not otherwise be able to see.
While it has been used by astronomers to observe far-away galaxies for a long time, finding these cosmic magnifying glasses in the first place has been hit-and-miss.
“These lenses are very small so if you have fuzzy images, you’re not going to really be able to detect them,” says Tran.
While these lenses let us see objects that are millions of light years away more clearly, it should also let us ‘see’ invisible dark matter that makes up most of the universe.
“We know that most of the mass is dark,” says Tran. “We know that mass is bending light and so if we can measure how much light is bent, we can then infer how much mass must be there.” Having many more gravitational lenses at various distances will also give us a more complete image of the timeline going back almost to the Big Bang.
“The more magnifying glasses you have, the better chance you can try to survey these more distant objects. Hopefully, we can better measure the demographics of very young galaxies,” says Tran.
Then somewhere between those really early first galaxies and Earth, there’s a whole lot of evolution that’s happening, with tiny star forming regions that convert pristine gas into the first stars to the sun, the Milky Way.”
“And so, with these lenses at different distances, we can look at different points in the cosmic timeline to track essentially how things change over time, between the very first galaxies and now.”
Tran’s team spanned the globe, with each group providing different expertise.
“Being able to collaborate with people, at different universities, has been so crucial, both for setting the project up in the first place, and now continuing with all of the follow-up observations,” she says.
Stuart Wyithe of the University of Melbourne and Director of ASTRO 3D says each gravitational lens is unique and tells us something new.
“Apart from being beautiful objects, gravitational lenses provide a window to studying how mass is distributed in very distant galaxies that are not observable via other techniques. By introducing ways to use these new large data sets of the sky to search for many new gravitational lenses, the team opens up the opportunity to see how galaxies get their mass,” he says.
Karl Glazebrook of Swinburne University, and Tran’s Co-Science Lead on the paper, paid tribute to the work that had gone before.
“This algorithm was pioneered by Colin Jacobs at Swinburne. He sifted through tens of millions of galaxy images to prune the sample down to 5,000. Never did we dream that the success rate would be so high,” he says.
“Now we are getting images of these lenses with the Hubble Space Telescope, they range from jaw-droopingly beautiful to extremely strange images that will take us considerable effort to figure out.”
Tucker Jones of UC Davis, another co-science lead on the paper, described the new sample as “a giant step forward in learning how galaxies form over the history of the universe.”
“Thanks to the lensing effect we can learn what these primitive galaxies look like, what they are made of, and how they interact with their surroundings.”
The study was conducted in collaboration with researchers from the University of New South Wales, Swinburne University of Technology, Australian National University, Curtin University, and the University of Queensland in Australia, the University of California, Davis, in the US, the University of Portsmouth, in the UK, and University of Chile.
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