Dark matter is the invisible force that holds the Universe together, or so we think. It makes up about 85% of all matter and about 27% of the contents of the Universe, but because we can’t see it directly, we have to study its gravitational effects on galaxies and other cosmic structures. Despite decades of research, the true nature of dark matter remains one of the most elusive questions in science.
According to one leading theory, dark matter may be a type of particle that barely interacts with anything other than gravity. But some scientists believe these particles might occasionally interact with each other, a phenomenon known as self-interaction. Detecting such interactions would offer crucial clues about the properties of dark matter.
The Mystery of the True Nature of Dark Matter
Distinguishing the subtle signals of dark matter self-interactions from other cosmic effects, such as those caused by active galactic nuclei (AGN), the supermassive black holes at the centers of galaxies, has been a major challenge. Feedback from AGN can push matter in ways similar to dark matter effects, making them difficult to distinguish.
In a significant step forward, astronomer David Harvey of EPFL’s Astrophysics Laboratory has developed a deep learning algorithm that can untangle these complex signals. The research was published in Nature Astronomy.
Their AI-based method is designed to distinguish between the effects of self-interactions of dark matter and AGN feedback by analyzing images of galaxy clusters, large collections of galaxies bound together by gravity. The breakthrough promises to dramatically improve the precision of dark matter studies.
Harvey trained a Convolutional Neural Network (CNN), a type of AI that is particularly good at recognizing patterns in images, with images from the BAHAMAS-SIDM project, which models galaxy clusters under different dark matter and AGN feedback scenarios. By being fed thousands of simulated images of galaxy clusters, the CNN learned to distinguish between signals caused by dark matter self-interactions and those caused by AGN feedback.
Of the various CNN architectures tested, the most complex, called “Inception,” also proved to be the most accurate. The AI was trained on two primary dark matter scenarios, characterized by different levels of self-interaction, and validated on additional models, including a more complex, velocity-dependent dark matter model.
Inception achieved an impressive 80% accuracy under ideal conditions, effectively identifying whether galaxy clusters were influenced by self-interacting dark matter or AGN feedback. It maintained its high performance even when researchers introduced realistic observational noise that mimics the type of data we expect from future telescopes like Euclid.
This means that Inception, and AI approaches more generally, could prove incredibly useful for analyzing the vast amounts of data we collect from space. Furthermore, AI’s ability to handle invisible data means it is adaptable and reliable, making it a promising tool for future dark matter research.
AI-based approaches like Inception could have a significant impact on our understanding of what dark matter actually is. As new telescopes gather unprecedented amounts of data, this method will help scientists sift through it quickly and thoroughly, potentially revealing the true nature of dark matter.
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