The SMU SuperCDMS Program

点击翻译按钮获取中文摘要

Astrophysical observations and cosmological data give strong evidence that nearly a quarter of the Universe consists of dark matter. Weakly interacting massive particles (WIMPs) and axions have been proposed as theoretically favored candidates for dark matter. Despite the strong evidence for the existence of dark matter, neither WIMPs nor axions have been conclusively detected. This award will provide funding for the research program at SMU that is dedicated to advancing the SuperCDMS SNOLAB experiment towards its goal of exploring the low mass ( 10 GeV/c^2) dark matter region, and ultimately to having sensitivity to WIMP-nucleon cross sections where solar neutrino-nucleus scattering becomes significant (the so-called “neutrino floor”). Part of achieving this objective is an already-planned upgrade of the experiment that will include more massive detectors made of Ge and Si crystals operated in both an ultra-sensitive high-voltage mode and standard voltage mode. The educational objective of this award is to create research opportunities for undergraduate students at SMU and foster relationships with the North Texas community. The approach is to enhance the Engaged Learning program at SMU by providing support for 1 - 2 undergraduate students to engage in research during both the summer and academic year. To foster relationships with the community of North Texas, the group will volunteer their time participating in a variety of mentoring activities.During the next three years, several critical milestones must be achieved to advance the SuperCDMS SNOLAB program. These include installing the SuperCDMS SNOLAB experiment, commissioning and operating the experiment, publishing results from early science taken at the CUTE detector test facility, and a surface assay of materials for the purpose of developing a radon background model. In addition, SuperCDMS must maintain a controlled low background environment, and characterize this background to a level that allows the collaboration to take advantage of more sophisticated analysis techniques that employ tools such as maximal likelihood estimations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.