AI 中文总结
研究 LDMX 对轻暗物质的发现潜力与模型选择能力，通过测量电子 - 核碰撞中反冲电子能量和横向动量探测，评估不同基准点发现潜力及排除极限，证明二维分析辨别暗区假设能力更强，结果在似然统计框架内得出。
AI 中文摘要
轻暗物质是解释观测遗迹丰度的一个引人注目的情景，基于加速器的搜索是一种强大的发现策略。即将开展的轻暗物质实验（LDMX）旨在通过测量高强度电子 - 核碰撞中反冲电子的能量和横向动量来探测轻暗物质。我们评估了 LDMX 在由暗光子介导的复杂标量暗物质的热目标上四个基准点处对轻暗物质的发现潜力，针对不同背景假设，并在一个代表性基准点评估其模型选择能力。发现 LDMX 在某些基准点沿遗迹目标在两种背景情景下都有很强的预计 5σ 发现潜力，还计算了假设无信号事件时的预计 90%置信水平排除极限。二维反冲电子分布的归一化和形状分别编码了耦合和暗光子质量，能在信号过量时进行参数推断。对模拟数据进行参数估计，发现沿遗迹目标两个参数都能在不确定性内恢复。评估数据能否区分相互竞争的暗区假设，特别是具有额外更高电磁矩相互作用的暗光子。证明使用贝叶斯因子的模型比较能在统计上区分暗区假设，二维分析比一维分析有更大的辨别能力。这些结果是在基于似然的统计框架内获得的，纳入了信号和背景建模及其相关系统不确定性，并采用了频率主义和贝叶斯方法。该框架设计用于直接应用于实际的 LDMX 数据。
英文摘要
Light dark matter (DM) is a compelling scenario for the observed relic abundance, with accelerator-based searches as a powerful discovery strategy. The upcoming Light Dark Matter eXperiment (LDMX) is designed to probe light DM by measuring the energy and transverse momentum of recoil electrons in high-intensity electron-nucleus collisions. We evaluate the discovery potential of LDMX to light DM at four benchmark points along the thermal target for complex scalar DM mediated by a dark photon, for different background assumptions, and assess its model selection power at a representative benchmark. We find that LDMX has strong projected 5$σ$ discovery potential along the relic target across both background scenarios for certain benchmarks, and we further compute projected 90% C.L. exclusion limits assuming no measured signal events. The normalization and shape of the two-dimensional recoil electron distribution encodes the coupling and dark photon mass, respectively, enabling parameter inference in the event of a signal excess. We perform parameter estimation on simulated data and find that both parameters are recovered within their uncertainties along the relic target. We assess whether the data can distinguish between competing dark sector hypotheses, in particular, dark photons with additional higher electromagnetic moment interactions. We demonstrate that model comparison using the Bayes factor allows dark sector hypotheses to be statistically distinguished, with the two-dimensional analysis affording substantially greater discriminating power than the one-dimensional analysis. These results are obtained within a likelihood-based statistical framework, incorporating signal and background modelling with their associated systematic uncertainties and employing both frequentist and Bayesian methods. The framework is designed for direct application to real LDMX data.
Comments35 pages, 16 figures
