论文标题
higgs使用深度学习$ b \ bar {b} b \ bar {b} $最终状态
Higgs self-coupling measurements using deep learning in the $b\bar{b}b\bar{b}$ final state
论文作者
论文摘要
测量Higgs三连线自耦合$λ_{HHH} $是实验要求的，但对于理解Higgs潜力的形状而言是基本的。我们使用四个$ b $ QUARK频道（$ hh \ to 4b $）中的Di-Higgs事件为HL-LHC提出了全面的分析策略，从而将当前方法扩展到多个方向上。我们执行深度学习，以抑制BSM $λ_{HHH} $方案的专用优化的强大多列背景。我们将$λ_{hhh} $使用大半径喷气机的不同多重性和两个统计结构的不同多重性进行了比较，该结构将$ h \ $ h \重建为bb $衰减。我们表明，SM Top Yukawa耦合中的当前不确定性$ y_t $可以通过$ \ sim 20 \％$修改$λ_{hhh} $约束。对于SM $ y_t $，我们发现$ -0.8<λ_{hhh} /λ_{hhh}^\ text {sm} <6.6 $在68％cl下的前景，在3000〜fb $^{ - 1} $ hl-lhc数据的简化假设下。我们的结果提供了对Di-Higgs识别和机器学习技术的仔细评估，以实现HIGGS自我耦合的全力测量，并提高了未来改进的要求。
Measuring the Higgs trilinear self-coupling $λ_{hhh}$ is experimentally demanding but fundamental for understanding the shape of the Higgs potential. We present a comprehensive analysis strategy for the HL-LHC using di-Higgs events in the four $b$-quark channel ($hh \to 4b$), extending current methods in several directions. We perform deep learning to suppress the formidable multijet background with dedicated optimisation for BSM $λ_{hhh}$ scenarios. We compare the $λ_{hhh}$ constraining power of events using different multiplicities of large radius jets with a two-prong structure that reconstruct boosted $h \to bb$ decays. We show that current uncertainties in the SM top Yukawa coupling $y_t$ can modify $λ_{hhh}$ constraints by $\sim 20\%$. For SM $y_t$, we find prospects of $-0.8 < λ_{hhh} / λ_{hhh}^\text{SM} < 6.6$ at 68% CL under simplified assumptions for 3000~fb$^{-1}$ of HL-LHC data. Our results provide a careful assessment of di-Higgs identification and machine learning techniques for all-hadronic measurements of the Higgs self-coupling and sharpens the requirements for future improvement.
