Efficient neutrino oscillation parameter inference using Gaussian processes
| Autor: | L. Li, N. Nayak, Pierre Baldi, Jianming Bian |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Physics
010308 nuclear & particles physics Inference Context (language use) Parameter space Physics::Data Analysis Statistics and Probability 01 natural sciences Confidence interval Neyman construction symbols.namesake 0103 physical sciences Statistical inference symbols 010306 general physics Neutrino oscillation Gaussian process Algorithm |
| DOI: | 10.5281/zenodo.4123609 |
| Popis: | The unified approach of Feldman and Cousins allows for exact statistical inference of small signals 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 relies on the Neyman construction of the classical confidence interval and is computationally intensive as it is typically done in a grid-based fashion over the entire parameter space. In this article, we propose an efficient algorithm for the Feldman-Cousins approach using Gaussian processes to construct confidence intervals iteratively. We 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 an accuracy above 99.5%. |
| Databáze: | OpenAIRE |
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