On August 17, 2017, a collision of two neutron stars was observed both by gravitational-wave detectors and by traditional telescopes. This first simultaneous observation of an astrophysical event by the two "messengers" (gravitational waves and light) has had broad scientific implications, ranging from tests of fundamental physical laws and expansion of the universe to the understanding of the origin of the heaviest elements in the periodic table. The event ushered in a new field, Multi-Messenger Astrophysics, with the potential to transform how researchers observe and understand the universe. In the coming years, vast amounts of new data are expected from existing and upcoming gravitational wave detectors, telescopes, gamma ray detectors, and neutrino detectors. The size, complexity, and diversity of the new datasets will be unlike anything seen in Astrophysics. This National Science Foundation Research Traineeship (NRT) award to the University of Minnesota Twin Cities will facilitate the management, processing, and analysis of these datasets by training MS and PhD students in modern data science techniques. The NRT will use the nascent field of Multi-Messenger Astrophysics as a training ground to prepare students for the challenges of the modern data-driven workforce in both industry and academia. This project anticipates training fifty (50) MS and PhD students, including thirty (30) funded trainees, from the fields of astrophysics, computer science, electrical engineering, physics and statistics. In addition, approximately eighty (80) graduate students are anticipated to participate in aspects of the technical, research, and professional training provided by the project.
The trainees will pursue unique research opportunities by working in interdisciplinary trainee teams, mentored by interdisciplinary faculty mentoring teams. They will use the most modern data analytics techniques, such as machine learning, deep learning, Bayesian inference and others, to address some of the pressing questions in Multi-Messenger Astrophysics. Their training will include a series of workshops, seminars, symposia, journal club, outreach activities, and fall retreats designed to provide trainees with a broader exposure to data science techniques, tools, and applications, and with opportunities for development of professional communications and leadership skills, and career development, vision, and impact skills. The trainees will pursue summer research and industry internships with local and national companies and research organizations. A series of activities will be pursued to ensure full participation of underrepresented minorities and women and to develop a strong community around this program that will provide a supportive structure for the trainees, helping them move through the program efficiently and presenting them with career opportunities post-graduation. The best practices developed by this program will be publicly available for adoption by other STEM graduate programs across the nation.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.
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.
