用机器学习提升对暗物质的对撞机探测能力，发现隐藏信号。
Shedding Light on Dark Matter at the LHC with Machine Learning
- 采用数据驱动的机器学习方法，增强对软光子信号的识别能力。
- 在100 fb⁻¹、14 TeV条件下，可探测质量达225 GeV的暗物质候选者。
- 适合关注对撞机新物理、暗物质探测的粒子物理研究者。
我们在具有$Z_3$对称性的次最小超对称标准模型（NMSSM）框架下研究一种由单态主导的轻量级超对称粒子（LSP）作为弱相互作用大质量粒子（WIMP）暗物质候选者。该模型在参数空间中存在通过与附近希格斯子类电弱费米子共湮灭产生暗物质、且直接探测信号被抑制的“盲点”区域。然而，由于希格斯子类中性微子向单态主导的LSP和光子的辐射衰变增强，对撞机信号仍具潜力。相较于双规和规范子类电弱费米子的MSSM情景，NMSSM能产生多重光子末态，形成独特信号。尽管衰变产物能量较低（质量差Δm < 12–20 GeV），背景干扰大，我们提出基于机器学习的数据驱动分析方法，显著提升灵敏度。在100 fb⁻¹、14 TeV的集成亮度下，该方法可实现5σ发现可达225 GeV（Δm ≲ 12 GeV），2σ排除上限达285 GeV（Δm ≲ 20 GeV）。结果表明，对撞机搜索可有效探测当前直接探测无法触及的暗物质候选者，推动LHC合作组采用机器学习方法开展相关搜索。
原文摘要 · Abstract (English)
We investigate a WIMP dark matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the $Z_3$-symmetric Next-to-Minimal Supersymmetric Standard Model (NMSSM). This framework gives rise to regions of parameter space where DM is obtained via co-annihilation with nearby higgsino-like electroweakinos and DM direct detection~signals are suppressed, the so-called ``blind spots''. On the other hand, collider signatures remain promising due to enhanced radiative decay modes of higgsinos into the singlino-dominated LSP and photons, rather than into leptons or hadrons. Compared to MSSM scenarios with light bino- and wino-like electroweakinos, the NMSSM allows for final states with multiple photons arising from cascade radiative decays, providing a distinctive collider signature. This motivates searches for radiatively decaying neutralinos, however, these signals face substantial background challenges, as the decay products are typically soft due to the small mass-splits ($Δm$) between the LSP and the higgsino-like coannihilation partners. We apply a data-driven Machine Learning (ML) analysis that improves sensitivity to these subtle signals, offering a powerful complement to traditional search strategies to discover a new physics scenario. Using an LHC integrated luminosity of $100~\mathrm{fb}^{-1}$ at $14~\mathrm{TeV}$, the method achieves a $5σ$ discovery reach for higgsino masses up to $225~\mathrm{GeV}$ with $Δm\!\lesssim\!12~\mathrm{GeV}$, and a $2σ$ exclusion up to $285~\mathrm{GeV}$ with $Δm\!\lesssim\!20~\mathrm{GeV}$. These results highlight~the power of collider searches to probe DM candidates that remain hidden from current~direct detection experiments, and provide a motivation for a search by the LHC collaborations using ML methods.
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