Scientists Use Artificial Intelligence to Estimate Dark Matter in the Universe
Scientists from the Department of Physics and the Department of Computer Science at ETH Zurich are using artificial intelligence to learn more about our universe. They are contributing to the methods used in order to estimate the amount of dark matter present. The group of scientists developed machine learning algorithms that are similar to those used by Facebook and other social media companies for facial recognition. These algorithms help analyze cosmological data. The new research and results were published in the scientific journal Physical Review D.
Tomasz Kacprzak, a researcher from the Institute of Particle Physics and Astrophysics, explained the link between facial recognition and estimating dark matter in the universe.
“Facebook uses its algorithms to find eyes, mouths or ears in images; we use ours to look for the tell-tale signs of dark matter and dark energy,” he explained.
Dark matter is not able to be seen directly by telescope images, but it does bend the path of light rays that are coming to earth from other galaxies. This is called weak gravitational lensing, and it distorts the images of those galaxies.
The distortion that takes place is then used by scientists. They build maps based on mass of the sky, and they show where dark matter is. The scientists then take theoretical predictions of the location of dark matter and compare them to the built maps, and they look for the ones that most match the data.
The described method with maps is traditionally done by using human-designed statistics, which help explain how parts of the maps relate to one another. The problem that arises with this method is that it is not well suited for detecting the complex patterns that are present in such maps.
“In our recent work, we have used a completely new methodology…Instead of inventing the appropriate statistical analysis ourselves, we let computers do the job,” Alexandre Refregier said.
Aurelien Lucchi and his team from the Data Analytics Lab at the Department of Computer Science, along with Janis Fluri, a PhD student from Refregier’s group and the lead author of the study, worked together using machine learning algorithms. They used them to establish deep artificial neural networks that are able to learn to extract as much information from the dark matter maps as possible.
The group of scientists first gave the neural network computer-generated data that simulated the universe. The neural network eventually taught itself which features to look for and to extract large amounts of information.
These neural networks outperformed the human-made analysis. In total, they were 30% more accurate than the traditional methods based on human-made statistical analysis. If cosmologists wanted to achieve the same accuracy rate without using these algorithms, they would have to dedicate at least twice the amount of observation time.
After these methods were established, the scientists then used them to create dark matter maps based on the KiDS-450 dataset.
“This is the first time such machine learning tools have been used in this context, and we found that the deep artificial neural network enables us to extract more information from the data than previous approaches. We believe that this usage of machine learning in cosmology will have many future applications,” Fluri said.
The scientists now want to use this method on bigger image sets such as the Dark Energy Survey, and the neural networks will start to take on new information about dark matter.