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June 22, 2020 to July 2, 2020
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Efficient neutrino oscillation parameter inference using Gaussian processes

Not scheduled


Mr Nitish Nayak (University of California-Irvine)


Feldman-Cousins method is a unified approach to create frequentist confidence intervals near physical limits or with low statistics. It has been widely used in oscillation parameter inference for neutrino experiments. However, the Feldman-Cousins method is usually 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 estimate a frequentist confidence contour for the neutrino oscillation parameters. We show that the Gaussian Process enhanced method significantly reduces the computational cost while producing the same result as the standard Feldman-Cousins method.


Gaussian Process boosts neutrino oscillation parameter inference with Feldman-Cousins approach

Primary authors

Prof. Jianming Bian (University of California, Irvine) Mr Nitish Nayak (University of California-Irvine)


Lingge Li (UCI) Prof. Pierre Baldi (UCI)

Presentation Materials