论文标题
通过人工智能和机器学习黑盒优化算法探索参数空间
Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms
论文作者
论文摘要
超出标准模型理论的约束通常涉及扫描高度多维参数空间，并根据实验界限和理论约束检查可观察到的预测。此类任务通常是及时且计算上昂贵的，尤其是当模型受到严格限制并因此导致非常低的随机采样效率时。在这项工作中，我们使用用于黑盒优化问题的人工智能和机器学习搜索算法解决了这一挑战。使用CMSSM和PMSSM参数空间，我们同时考虑Higgs质量和暗物质遗物密度约束来研究其采样效率和参数空间覆盖范围。我们发现我们的方法可以产生采样效率的数量级提高，同时合理涵盖参数空间。
Constraining Beyond the Standard Model theories usually involves scanning highly multi-dimensional parameter spaces and check observable predictions against experimental bounds and theoretical constraints. Such task is often timely and computationally expensive, especially when the model is severely constrained and thus leading to very low random sampling efficiency. In this work we tackled this challenge using Artificial Intelligence and Machine Learning search algorithms used for Black-Box optimisation problems. Using the cMSSM and the pMSSM parameter spaces, we consider both the Higgs mass and the Dark Matter Relic Density constraints to study their sampling efficiency and parameter space coverage. We find our methodology to produce orders of magnitude improvement of sampling efficiency whilst reasonably covering the parameter space.
