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交叉验证在随机分析的分析延续
Gabe Schumm1, Sibin Yang1, Anders W Sandvik1
1Department of Physics, <a href="https://ror.org/05qwgg493">Boston University</a>, 590 Commonwealth Avenue, Boston, Massachusetts 02215, USA.
Physical review. E
|December 18, 2024
概括
量子蒙特卡洛 (QMC) 数据的随机分析延续 (SAC) 现在可以更准确地识别光谱函数. 一种新的交叉验证技术有助于从多种可能性中选择最佳频谱.
科学领域:
- 计算物理 计算物理
- 量子多体理论 量子多体理论
- 统计力学 统计力学
背景情况:
- 随机分析延续 (SAC) 对于将量子蒙特卡洛 (QMC) 数据与可测量的动态响应函数联系起来至关重要.
- 最近的SAC进步使得光谱函数的高保真分辨率具有尖的特征,如峰值和边缘.
- 分析延续的错误性往往导致多个有效的光谱表示.
研究的目的:
- 引入一种无偏的交叉验证技术,从各种参数化和约束中选择最可能的光谱函数.
- 通过结合机器学习和统计学的模型选择原则来提高随机分析延续的可靠性.
主要方法:
- 实施一种根据机器学习和统计学调整的交叉验证技术.
- 该方法应用于来自QMC模拟的虚拟时间数据.
- 使用人工光谱生成的合成数据进行测试,以验证性能.
主要成果:
- 证明交叉验证技术在确定最可能的频谱方面的有效性.
- 成功应用于QMC生成和合成数据.
- 验证该方法处理具有尖特征的光谱函数的能力.
结论:
- 拟议的交叉验证方法为分析延续中的模型选择提供了公正的方法.
- 这种技术显著改善了从数值数据中识别光谱特征.
- 该程序广泛适用于SAC以外的各种数值分析连续方法.
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