The Korean Physical Society 06130 22, Teheran-ro 7-gil, Gangnam-gu, Seoul, Republic of Korea 610 Representation : Tae Won NOH TEL: 02-556-4737 FAX: 02-554-1643 E-mail : Copyright(C) KPS, All rights reserved.
30 May 2022 to 4 June 2022
Virtual Seoul
Asia/Seoul timezone

Efficient Neutrino Oscillation Parameter Inference with Gaussian Process

Not scheduled
Virtual Seoul

Virtual Seoul

Poster Neutrino oscillation Poster


Yiwen Xiao (University of California Irvine)


The unified approach of Feldman and Cousins allows for estimating confidence intervals for datasets with small statistics that commonly arise in high energy physics. It has gained widespread use, for instance, in measurements of neutrino oscillation parameters in long-baseline experiments. However, the approach is computationally intensive as it is typically done in a grid-based fashion over the entire parameter space. In this poster, I will discuss a more efficient algorithm for the Feldman-Cousins approach using Gaussian processes to construct confidence intervals iteratively. I'll show that in the neutrino oscillation context, one can obtain confidence intervals fives times faster in one dimension and ten times faster in two dimensions while maintaining accuracy above 99.5%. I'll also discuss the next steps related to the implementation in the NOvA FC framework at NERSC.

Primary author

Yiwen Xiao (University of California Irvine)


Prof. Bian Jianming (University of California Irvine) Dr Li Lingge (University of California Irvine) Prof. Baldi Pierre (University of California Irvine) Dr Nitish Nayak (University of California Irvine)

Presentation Materials