利用合成数据验证自动化共振评估
Validating Automated Resonance Evaluation with Synthetic Data
AI总结：
针对核数据评估的主观偏差问题，本文开发了含无先验自动化拟合方法的测试框架，用高保真合成数据和受控实验优化拟合，还提出误差度量指标以提升评估的客观性与可复现性。
摘要：
核数据的完整性与精确度对从国家安全、核反应堆设计到医学诊断的广泛应用至关重要，其相关不确定性会显著影响应用结果。核数据中的很大一部分不确定性源于评估过程中的主观偏差，而评估是核数据生产流程中的关键阶段。近期研究进展表明，将特定流程自动化可减轻这类偏差，从而实现评估流程的标准化、降低不确定性并提升可复现性。\n本文致力于开发一套用于自动化评估技术测试的框架，重点关注无需用户提供任何先验信息的自动化拟合方法。该方法简化了评估流程，减少了初始评估阶段所需的人工工作量。研究凸显了该框架验证与优化子例程的能力，目标是利用高保真合成数据（带标记的实验数据）和完全受控计算实验的概念，对拟合流程进行性能分析与优化。\n研究还引入了一种误差度量指标，通过量化指定能量范围内的准确度与性能，为拟合质量提供清晰直观的衡量标准。该指标为流程的比较优化或超参数选择设定了标尺，完善了整个评估流程的方法论，并提升了可复现性与客观性。
英文摘要：
The integrity and precision of nuclear data are crucial for a broad spectrum of applications, from national security and nuclear reactor design to medical diagnostics, where the associated uncertainties can significantly impact outcomes. A substantial portion of uncertainty in nuclear data originates from the subjective biases in the evaluation process, a crucial phase in the nuclear data production pipeline. Recent advancements indicate that automation of certain routines can mitigate these biases, thereby standardizing the evaluation process, reducing uncertainty and enhancing reproducibility. This article contributes to developing a framework for automated evaluation techniques testing, emphasizing automated fitting methods that do not require the user to provide any prior information. This approach simplifies the process and reduces the manual effort needed in the initial evaluation stage. It highlights the capability of the framework to validate and optimize subroutines, targeting the performance analysis and optimization of the fitting procedure using high-fidelity synthetic data (labeled experimental data) and the concept of a fully controlled computational experiment. An error metric is introduced to provide a clear and intuitive measure of the fitting quality by quantifying the accuracy and performance across the specified energy. This metric sets a scale for comparison and optimization of routines or hyperparameter selection, improving the entire evaluation process methodology and increasing reproducibility and objectivity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
