减少稀有事件搜索中与模拟相关的排放：通过优化事件偏倚
Reducing simulation-related emissions in rare-event searches through optimised event biasing
- University of Birmingham, School of Physics and Astronomy（伯明翰大学物理与天文学学院）
- University of Hamburg, Institute for Experimental Physics（汉堡大学实验物理研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对稀有事件搜索中屏蔽模拟的重要性分裂偏倚技术进行优化，在统计精度与CPU时间间取得平衡，以减少模拟相关碳排放。
AI中文摘要：
稀有事件搜索实验继续将其灵敏度扩展到前所未有的水平。这要求越来越低的本底，以及能够将外部本底抑制数个数量级的屏蔽方案。此外，需要详细的模拟来充分理解这些实验本底。然而，研究表明，模拟和计算相关任务约占粒子物理学家平均碳足迹的10%。由于本底经常低于每千克靶材每千电子伏能量0.01个计数，模拟需要更多的计算资源，从而增加了稀有事件研究人员的计算相关碳足迹。事件偏倚通常作为屏蔽模拟中的补救措施，但缺乏关于如何最大化利用它们的系统性指导。本文讨论了一项针对重要性分裂偏倚技术的优化研究，重点是在统计精度与模拟CPU时间之间取得平衡，以及这如何有益于研究人员的平均碳足迹。
英文摘要：
Rare-event search experiments continue to extend their sensitivities to unprecedented levels. This requires increasingly small backgrounds, and shielding schemes that can suppress external backgrounds by several orders of magnitude. Moreover, detailed simulations are required to attain a good understanding of these experimental backgrounds. Studies have suggested, however, that simulations and computing-related tasks contribute approximately 10\% to the average particle physicist's carbon footprint. With backgrounds frequently below 0.01 counts per kg of target per keV of energy, simulations require more computing resources; growing a rare-event researcher's computing-related carbon footprint. Event biasing is often applied in shielding simulations as a remedy, yet there is a lack of systematic guidance on how to maximally benefit from them. An optimisation study for the importance-splitting biasing technique is discussed, focused on balancing statistical precision with simulation CPU-time, and how this can benefit the average researcher's carbon footprint.
