Physicists are advancing efforts to detect dark matter using innovative technology, spearheaded by Ashutosh Kotwal from Duke University. Kotwal’s team is developing a silicon-based “camera trap” designed to capture fleeting evidence of dark matter. This device will rely on a sophisticated algorithm to rapidly process vast amounts of visual data produced by the Large Hadron Collider (LHC), a major particle accelerator located underground between France and Switzerland.
“Our job is to ensure that if dark matter production is happening, then our technology is up to snuff to catch it in the act,” Kotwal said.
The LHC accelerates protons to near-light speeds and collides them with immense force, simulating conditions similar to the Big Bang. This results in a burst of subatomic particles, within which scientists hope to find hints of dark matter. Although dark matter constitutes about five times more mass than visible matter and influences galaxy formation through its gravitational pull, it remains invisible and undetectable by conventional means.
“There, researchers are looking for dark matter and other mysteries using detectors that act like giant 3D digital cameras, taking continuous snapshots of the spray of particles produced by each proton-proton collision,” the press release explained.
To uncover dark matter, researchers use detectors akin to giant 3D digital cameras to capture the chaotic aftermath of proton collisions. Despite this, dark matter’s presence is inferred rather than directly observed, often through the behavior of other particles. A key hypothesis is that dark matter might be signaled by the disappearance of heavy charged particles shortly after collisions. These particles could vanish within 10 inches of travel, possibly transforming into dark matter, and leaving behind a unique “disappearing track” in the detector.
“Most of these images don’t have the special signatures we’re looking for. Maybe one in a million is one that we want to save,” said Kotwal.
“To do that in real time, and for months on end, would require an image recognition technique that can run at least 100 times faster than anything particle physicists have ever been able to do,” Kotwal added in the press release.
The challenge is formidable due to the enormous volume of data generated—millions of images per second—with only a rare fraction potentially indicating dark matter. The new AI algorithm developed by Kotwal’s team, named the “track trigger,” is crucial in this process. It aims to swiftly identify and highlight possible dark matter signals from the data before subsequent collisions occur. The algorithm, incorporating advanced AI processors, is designed to analyze images in under 250 nanoseconds, efficiently filtering out irrelevant information.
The researchers are working towards a prototype of this device by summer 2024, with a fully operational system expected at the LHC in three to four years. The integration of around 2000 chips in the final product will significantly enhance the ability to detect dark matter, pushing the boundaries of current particle physics technology.