Applying Machine Learning to Many-Body Studies of Infinite Nuclear Matter

Neutron stars are extremely cold and dim, making observational astronomy difficult. Therefore, the only way to study them is through many-body studies of their constituent particles using state-of-the-art many-body methods with modern nuclear forces. However, many-body computations of infinite nuclear matter involve a large number of particles and complex potentials. This has a high computational cost, making large studies difficult. Machine learning is emerging as a useful tool in physics that will allow us to tackle problems which are difficult to solve with traditional computational methods. This talk will explore ways in which machine learning can be used to apply many-body methods to infinite nuclear matter to perform faster calculations with physically relevant accuracy. Ridge regression and kernel ridge regression will be applied using a variety of algorithms to find converged correlation energies, extrapolate to the thermodynamic limit, and to find coupled cluster correlation energies using only data from MBPT calculations. Accuracy compared to full calculations and time savings will be presented to justify the use of machine learning as a valid computational method for these calculations. This project is funded by NSF Grants No. PHY-1404159 and PHY-2013047.