Binning-Independent Bayesian Analysis of Time-Dependent Perturbed Angular Distribution data

2026-09-16

AI Summary

提出了一种新的时间依赖扰动角分布数据分析方法，通过构建基于单事件条件概率的似然函数避免了数据分箱，并在贝叶斯框架下计算感兴趣的g因子后验概率密度函数。

Abstract

We propose a new approach for analyzing Time-Dependent Perturbed Angular Distribution data. In this, a likelihood is constructed with the help of conditional probabilities of single events and binning of time data is avoided. This likelihood is used in a Bayesian framework to calculate the posterior probability density function for the $g$ factor of interest. This approach is compared to more traditional approaches that use binned data by analyzing simulated datasets. In many cases the resulting posterior probability densities are observed to be multimodal and the results can thus often not be summarized with a single Gaussian approximation. We find that results from the new approach are more reliable for low-statistics datasets.

Problem

• Time-Dependent Perturbed Angular Distribution
• binning
• Bayesian analysis
• low-statistics datasets

Innovation

• Binning-Independent
• Bayesian Analysis
• Time-Dependent Perturbed Angular Distribution
• Conditional Probabilities
• Multimodal Posterior