标题： 探索Scotogenic参数空间和使用多目标搜索算法绘制未探索的暗物质现象地图 显示英文标题

标题： Exploring Scotogenic Parameter Spaces and Mapping Uncharted Dark Matter Phenomenology with Multi-Objective Search Algorithms

摘要： 我们提出了一种新颖的人工智能方法，通过利用多目标优化算法来探索标准模型参数空间之外的区域。我们将这种方法应用于一个非最小阶子模型，该模型受到希格斯质量、μ子反常磁矩、暗物质遗留密度、暗物质直接探测、中微子质量和混合以及轻子味破坏过程的约束。我们的结果显示，成功扩展了之前工作中提出的理论实现。我们将多目标和单目标算法进行比较，并观察到前者提供了更丰富的理论多样性解决方案和改进的搜索能力。我们使用新颖性检测进一步探索了理论兴趣中稀疏分布的区域。这些结果表明了一种强大的搜索策略，该策略结合了多目标优化的全局探索能力和单目标优化的局部开发能力。 显示英文摘要

摘要： We present a novel artificial intelligence approach to explore beyond Standard Model parameter spaces by leveraging a multi-objective optimisation algorithm. We apply this methodology to a non-minimal scotogenic model which is constrained by Higgs mass, anomalous magnetic moment of the muon, dark matter relic density, dark matter direct detection, neutrino masses and mixing, and lepton flavour violating processes. Our results successfully expand on the phenomenological realisations presented in previous work. We compare between multi- and single-objective algorithms and we observe more phenomenologically diverse solutions and an improved search capacity coming from the former. We use novelty detection to further explore sparsely populated regions of phenomenological interest. These results suggest a powerful search strategy that combines the global exploration of multi-objective optimisation with the exploitation of single-objective optimisation.