标题： 似然比框架在高度动机的次主导信号中的应用 显示英文标题

标题： A Likelihood Ratio Framework for Highly Motivated Subdominant Signals

摘要： 在粒子物理学和宇宙学中，区分微妙的新物理信号与已确立的背景始终是个体现象学家面临的基本挑战。 本文提出了一种简单且稳健的统计框架，用于评估高度动机（HM）理论模型与实验结果残差之间的兼容性，重点关注数据似乎与背景预测一致的情景。 我们开发了一种似然比检验程序，用于比较零假设和备择假设，强调新物理引入的偏差相对于背景较小的情况。 我们通过两个具体的例子来演示这种方法，即局部过剩和振荡背景上的调制。 我们推导出明确的条件，在这些条件下必须考虑背景对数据残差的影响。 突出展示了该框架的实际应用，并讨论了除了局限性之外简化复杂背景建模的策略。 显示英文摘要

摘要： In particle physics and cosmology, distinguishing subtle new physics signals from established backgrounds is always a fundamental challenge for individual phenomenologists. This paper presents a simple and robust statistical framework to evaluate the compatibility of highly motivated (HM) theoretical models with the residual of the experimental results, focusing on scenarios where data appears consistent with background predictions. We develop a likelihood ratio test procedure that compares null and alternative hypotheses, emphasizing cases where new physics introduces small deviations from the background. We demonstrate the approach through two concrete examples, a localized excess and a modulation over an oscillatory background. We derive explicit conditions under which the effect of the background on the residual of the data must be accounted for. The framework's practicality is highlighted and in addition to the limitation, strategies to simplify complex background modeling are discussed.