Efficient Neutrino Oscillation Parameter Inference with Gaussian Process - Li, Lingge; Nayak, Nitish; Bian, Jianming; Baldi, Pierre (Proceedings of the AAAI Conference on Artificial Intelligence, 2019)
Many experiments have been set-up to measure the parameters governing the neutrino oscillation probabilities accurately, with implications for the fundamental structure of the universe. Very often, this involves inferences from tiny samples of data which have complicated dependencies on multiple oscillation parameters simultaneously. This is typically carried out using the unified approach of Feldman and Cousins which is very computationally expensive, on the order of tens of millions of CPU hours. In this work, we propose an iterative method using Gaussian Process to efficiently find a confidence contour for the oscillation parameters and show that it produces the same results at a fraction of the computation cost.