The Science
The Impact
Understanding the nature and origin of dark matter would completely revolutionize scientists’ understanding of the universe. Many theoretical models of dark matter predict that its signals can be detected using low-background radiation detectors. By looking for specific types of dark matter and finding no signal, scientists operating the Majorana Demonstrator experiment have significantly narrowed the characteristics of potential dark matter particles. Although the study did not directly detect dark matter, it used an approach that can help guide future experiments.
Summary
In this research, researchers used an advanced experimental setup with high-purity germanium detectors to search for several types of dark matter, finding no significant signal predicted by several theoretical models. The experiment was conducted at the Sanford Underground Research Facility, a Department of Energy Office of Science user facility. A range of universities and laboratories collaborated to conduct the experiment, making a broad, interdisciplinary effort. The scientific focus was on searching for distinct types of elusive dark matter candidates, including sterile neutrinos and bosonic and fermionic dark matter. If dark matter is ever detected, it would provide dramatic insight into the composition of the universe and physics beyond the Standard Model.
The research also reinforces the Majorana Demonstrator experiment’s incredible sensitivity and broad reach to multiple fields of physics. Several important research projects have all used the same underlying Majorana Demonstrator data set.
Funding
This material is based on work supported by the Department of Energy (DOE) Office of Science, Office of Nuclear Physics. The research was also supported by the National Science Foundation’s Particle Astrophysics Program and Nuclear Physics Program. The researchers also received support from the South Dakota Board of Regents Competitive Research Grant, the Laboratory Directed Research & Development programs at Lawrence Berkeley National Laboratory and Los Alamos National Laboratory, the Natural Sciences and Engineering Research Council of Canada, and the Canada Foundation for Innovation John R. Evans Leaders Fund. This research used resources provided by the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory and by the National Energy Research Scientific Computing Center, DOE Office of Science user facilities.