APRIL 12, 2018
Hunter Gabbard (BS physics ’16), a University of Glasgow School of Physics and Astronomy postgraduate student, and two of his classmates “have developed a sophisticated artificial intelligence which could underpin the next phase of gravitational wave astronomy.”
In a new paper published April 9 in Physical Review Letters—the world’s premier physics letter journal and the APS Physics’s flagship publication—the “researchers discuss how they used artificial intelligence tools to train an AI ‘brain’ to search for gravitational wave signals.”
“Hunter Gabbard said:
‘Deep learning algorithms involve stacked arrays of processing units, which we call neurons, which act as filters for the input data. Supervised deep learning allows us to ‘teach’ the system through three datasets we provide. The first dataset, the training set, allows us to ensure it’s ‘learning’ what we want. The second, the validation set, shows us it’s learning in the way we expect. The final set, the test set, helps us quantify the system’s performance.
‘What makes this process faster and more efficient than matched-filtering is that the training set is where all the computationally intensive activity occurs. Once the deep learning algorithm learns what to look for in a signal, it has the potential to be orders of magnitude faster than other methods.'”
Read Machine learning could help search for gravitational waves>>
Read news about Hunter Gabbard while he attended the University of Mississippi:
UM LIGO Among Recipients of $3 Million Award
Special Breakthrough Prize in Fundamental Physics backed by Silicon Valley entrepreneurs
UM Student Physicist Awarded Second Fulbright of the Year
Hunter Gabbard is a member of the LIGO group that helped discover gravitational waves