Testable likelihoods for beyond-the-standard model fits
Testable likelihoods for beyond-the-standard model fits
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DOI：
10.1140/epjc/s10052-023-12294-0
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
A. Beck;M. Reboud;D. van Dyk
中科院分区：
文献类型：
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作者：
A. Beck;M. Reboud;D. van Dyk
在精度前沿研究潜在的BSM效应需要准确地将信息从低能测量转移到高能BSM模型。我们建议使用归一化流来构造实现这种转移的似然函数。以这种方式构造的似然函数提供了生成额外样本的方法，并允许以检验统计量的形式进行“平凡”拟合优度检验。在这里，我们研究了一种特殊形式的归一化流，将其应用于多模态和非高斯的例子，并量化似然函数及其测试统计量的准确性。
Studying potential BSM effects at the precision frontier requires accurate transfer of information from low-energy measurements to high-energy BSM models. We propose to use normalising flows to construct likelihood functions that achieve this transfer. Likelihood functions constructed in this way provide the means to generate additional samples and admit a “trivial” goodness-of-fit test in form of atest statistic. Here, we study a particular form of normalising flow, apply it to a multi-modal and non-Gaussian example, and quantify the accuracy of the likelihood function and its test statistic.