通过LHC数据和机器学习, CERN的ATLAS实验没有发现超对称性的证据. CERN's ATLAS experiment finds no evidence of supersymmetry, setting tighter limits on hypothetical particles using LHC data and machine learning.

通过机器学习和大强子对撞机2015年至2018年的数据, CERN的ATLAS协作已经为超对称粒子设定了一些最强大的限制. The ATLAS Collaboration at CERN has set some of the strongest limits yet on supersymmetric particles using machine learning and data from the Large Hadron Collider’s 2015–2018 run. 研究人员寻找了充电子和中性子分解为低能粒子的迹象,包括从pions和两个低动量子"消失的痕迹",但没有发现超对称性的证据. Researchers searched for signs of charginos and neutralinos decaying into low-energy particles, including "disappearing tracks" from pions and two low-momentum leptons, but found no evidence of supersymmetry. 结果对这些假设粒子的质量和寿命有了更严格的限制,提高对微弱信号的灵敏度,并指导LHC及其他地方未来的实验. The results tighten constraints on the masses and lifetimes of these hypothetical particles, improving sensitivity to faint signals and guiding future experiments at the LHC and beyond.