EAGER: Enhanced sensitivity of Dark Matter Detectors via Machine Learning

点击翻译按钮获取中文摘要

Multiple astronomical observations have established that about 85% of the matter in the universe is not made of known elementary particles. Deciphering the nature of this so-called Dark Matter (DM) is of fundamental importance to cosmology, astrophysics, and high-energy particle physics. Directional dark matter detectors have access to a smoking-gun signature of dark matter – an order unity asymmetry in the angular distribution of recoils induced by Weakly Interacting Massive Particles (WIMPs). Although the leading limits on WIMP dark matter currently come from non-directional experiments, these experiments are rapidly approaching the solar neutrino floor, where the signal will be dominated by neutrinos from the sun and which will make future advances with those technologies more challenging. Directional detectors, however, can reach below the neutrino floor to constrain WIMP dark matter. This EAGER award will leverage Machine Learning (ML) techniques to further enhance the sensitivity of directional DM experiments. The ML techniques and analyses developed under this award would be broadly useful to experiments that employ gas-based Time Projection Chambers. Broader impacts of this work also include the training of a culturally and socioeconomically diverse set of female undergraduate students at Wellesley College, and the enhancement of the physics curriculum through the integration of particle physics experimentation in both teaching and research laboratories. Wellesley is a women's college traditionally ranked in the top 10 for ethnic diversity among liberal arts colleges. By integrating students at Wellesley in all aspects of this experimental particle physics program, the proposed work will broaden the participation of members of underrepresented groups in physics.This work will be applied to existing DRIFT (Directional Recoil Identification From Tracks) data and will establish an analysis pipeline that can be used with new data from directional experiments. Preliminary work using very basic ML techniques has already shown significant improvement in the nuclear recoil detection efficiency, with associated gains in the sensitivity to the WIMP-nucleon cross section. Under this award, the group will undertake a more complete exploration of the ML landscape to further enhance the sensitivity of DRIFT to dark matter.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.