用机器学习提升对暗物质衰变信号的探测能力。
Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
- 采用机器学习方法分析超对称框架下中性子的辐射衰变信号。
- 相比传统方法，可覆盖更多未被探索的参数空间。
- 适合从事暗物质搜寻与粒子物理机器学习研究者。
寻找弱相互作用大质量粒子（WIMPs）是高亮度大型强子对撞机（HL-LHC）的核心目标之一。本文利用机器学习技术研究超对称框架下第二轻量中性子通过辐射衰变为光子和最轻中性子（暗物质候选者）的信号。该模型中，最轻中性子可通过与次轻中性子及最轻带电伙伴的共湮灭机制满足观测到的暗物质丰度。同时，直接探测的暗物质-核子散射截面符合当前实验限制，并在相同参数区域增强辐射衰变过程。这一动机强烈支持对辐射衰变中性子的搜寻，但面临严峻背景干扰。我们比较了基于判据与机器学习的方法在探测此类粒子时的性能，评估其在这一合理新物理场景下的发现潜力。结果表明，使用机器学习可覆盖多数其他搜索手段未触及的参数空间。
原文摘要 · Abstract (English)
The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario. We demonstrate that using ML techniques would enable access to most of the parameter space unexplored by other searches.
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