基于最优输运的相空间包容性品质因子用于验证蒙特卡罗重加权
A Phase-Space Inclusive Figure of Merit Based in Optimal Transport for Validating Monte Carlo Reweightings
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中文总结 AI 辅助
本文提出基于最优输运的截面移动者距离，用于无分箱验证蒙特卡罗重加权方案，可量化理论预测间转换工作量，并有效基准测试减轻负权重影响的方案。
中文摘要 AI 辅助
验证模型在完整相空间重加权后是否保留了其底层物理特性，这一任务具有独特的挑战性。通常，验证依赖于比较一维可观测量的直方图；然而，这种方法可能掩盖完整预测中的相关性和偏差。我们提出了一种新颖的、无分箱的方法来比较此类重加权方案的性能，该方法基于“截面移动者距离”，这是最优输运的一种应用，用于量化将一个理论预测转换为另一个理论预测所需的工作量，并使得结果能够在度量空间的意义上进行解释。我们展示了该方法在基准测试各种减轻蒙特卡罗模拟中负权重影响的重加权方案时的实用性。该方法可广泛应用于其他需要以无分箱方式研究完整相空间重加权方案中偏差的场景。
英文摘要
Validating whether the underlying physics of a model has been retained after a full phase-space reweighting poses a unique challenge. Often, validation relies on comparing histograms of 1D observables; however, this can mask correlations and biases in the complete prediction. We present a novel, unbinned approach to comparing the performance of such reweighting schemes based on the "Cross-Section-Mover's Distance", an application of Optimal Transport that quantifies the work required to transform one theoretical prediction into another and enables an interpretation of results in terms of metric spaces. We demonstrate its utility when benchmarking various reweighting schemes that mitigate the effects of negative weights in a Monte Carlo simulation. This approach can be broadly applied in other scenarios where biases in full phase-space reweighting schemes should be studied in an unbinned way.
发表机构
- Brown University（布朗大学）
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