Ｔ２ＫとＳＫ実験データの組合せによるニュートリノ質量とミキシングパラメータの制限

Richard Calland开发了一种利用图形处理单元（gpu）重建超级神冈探测器中中微子相互作用事件的新方法。该方法测试了不同事件拓扑和粒子运动学假设下的电荷和到达时间似然。为了找到最大似然，Richard开发了一种新的跨维可逆跳跃马尔可夫链蒙特卡罗算法。考虑到超级神冈中大量的光倍增管（PMT）（~11,000），有必要使用gpu来计算每个PMT在变化的每个步骤中的电荷和到达时间可能性。利用这笔研究经费，理查德在Kavli IPMU建立了一个GPU集群来开展他的工作。通过在每个GPU上使用1000-2000个逻辑核，与使用cpu相比，计算时间可以提高50倍。Richard还展示了可逆跳跃马尔可夫链蒙特卡罗方法重建具有大量粒子的事件拓扑的改进能力。他的工作将应用于多粒子事件的重建，这对于利用超级神冈的大气中微子相互作用确定中微子的质量顺序至关重要。理查兹应邀在物理统计和中微子实验事件分类方法研讨会上发表了他的工作，题为“中微子事件重建中的统计问题”。

Richard Calland has developed a new method for reconstructing neutrino interaction events in the Super-Kamiokande detector using graphics processing units (GPUs). The method tests the charge and arrival time likelihoods for different hypotheses of the event topology and particle kinematics. To find the maximum likelihood, Richard developed a novel trans-dimensional reversible jump Markov chain Monte Carlo algorithm. Given the large number of photo-multiplier tubes (PMTs) in Super-Kamiokande (~11,000), it was necessary to use GPUs to calculate the charge and arrival time likelihoods for each PMT at each step in the change. Using this research grant, Richard built a GPU cluster at Kavli IPMU to carry out his work. By using the 1000-2000 logical cores on each GPU, the calculation time could be improved by a factor of 50 compared to using CPUs. Richard also showed the improved ability of the reversible jump Markov chain Monte Carlo method to reconstruct event topologies with a large number of particles. His work will be applied to the reconstruction of multi-particle events that are critical for the determination of the neutrino mass ordering using atmospheric neutrino interactions in Super-Kamiokande. Richards was invited to present his work at the PhyStat-nu workshop on statistics and event classification methods in neutrino experiments in a talk titled, "Statistical Issues in Neutrino Event Reconstruction.