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在质子-质子碰撞中寻找与异常喷气子结构的喷气共振的模拟无意识搜索 ats= 13 TeV
1European Organization for Nuclear Research, HCP, CH-1211 GENEVE 23, Geneva, 1211 Geneva 23, SWITZERLAND.
概括
研究人员使用先进的机器学习在喷射事件中寻找新的物理信号. 没有发现有意义的证据,但这些方法提高了对潜在的新物理特征的灵敏度.
科学领域:
- 高能物理 高能物理
- 粒子物理学 粒子物理学
- 碰撞器物理学 碰撞器物理学
背景情况:
- 粒子物理学的标准模型非常成功,但无法解释诸如暗物质或暗能量的现象.
- 寻找新物理学的研究往往侧重于标准模型预测的偏差,例如新重粒子 (共振) 的产生.
- 喷射器的最终状态是在大强子对撞机 (LHC) 寻找新共振的常见道.
研究的目的:
- 为了在特定质量范围 (1.8-6 TeV) 内的喷射事件中进行模拟无意识的狭窄共振搜索.
- 利用机器学习技术识别具有异常子结构的喷流,表明新的物理.
- 通过使用各种异常检测算法来最大限度地提高对未知的新物理特征的灵敏度.
主要方法:
- 在dijet最终状态中进行了对狭窄共振的模型不可知搜索.
- 使用多变量机器学习方法来选择具有异常子结构的喷气式飞机.
- 无监督,弱监督和半监督的异常检测算法应用于CMS数据 (138 fb-1在13 TeV).
主要成果:
- 在分析的数据中没有观察到高于预期背景的显著超值.
- 对于基准信号模型的生产截面,已经得出了排除限制.
- 在基准模型上报告的几个排除限制是首次出现的.
结论:
- 与传统的搜索策略相比,应用的异常检测方法显著提高了对各种新物理模型的灵敏度.
- 虽然没有发现新的共振,但这项研究表明了数据驱动,模型不可知的方法在寻找新物理学的力量.
- 这些发现为新的物理场景提供了有价值的约束,并为未来使用先进的计算技术进行搜索铺平了道路.
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