dark matter in space

Undergraduate Trains Sensors to Recognize Elusive Dark Matter

October 20, 2020

The Department of Energy awarded University of Colorado Denver’s Anthony Villano and Kitty Harris, as well as their colleagues at the University of Minnesota (UMN), a $240,000 grant over the next two years to explore a new technique that may help scientists hunting dark matter.

Called “Extremely Low Energy Nuclear Recoil Calibrations for Dark Matter Direct Detection,” the project will commence in the coming weeks.

Villano, PhD, assistant professor in the Department of Physics at CU Denver, has been working with sensitive cryogenic dark matter sensors for over a decade and throughout that time, he wondered: can we train sensors to be more adept at seeing dark matter? He says it’s akin to finding “dark matter goggles.”

Training Sensors to “See” Dark Matter

Villano and colleagues at UMN developed a technique to train sensors by mimicking what a dark matter particle might look like to their sensors. The technique utilizes neutrons, particles which are much easier to find than dark matter.

“It’s not uncommon in dark matter research to use neutrons for something like this,” said Villano. “It’s the technique that’s news.”

The project hinges on slower moving, room temperature neutrons called thermal neutrons. The slow-moving neutrons are essentially invisible to dark matter sensors until the nucleus absorbs them, which then “tickles” the sensors.

This summer, Harris, an undergraduate at CU Denver, worked out the physics of how the thermal neutrons “tickle” the sensor the way a dark matter particle might. This will mimic what scientists could expect when a real dark matter particle is detected. Harris is using that work to prove the technique in an upcoming publication with Villano and UMN colleagues.

Harris also laid the groundwork for going one step further: proving the technique is the best way to train not just cryogenic sensors, but possibly hundreds of others, which could alter the field of dark matter detection.