论文标题
使用神经网络的Katrin快速而精确的模型计算
Fast and precise model calculation for KATRIN using a neural network
论文作者
论文摘要
我们提出了一种快速而精确的方法，用于近似使用神经网络的Karlsruhe Tritium Neutminino（Katrin）实验的物理模型。 Katrin旨在使用Beta-Decay的运动学测量有效的电子抗神经质量，其灵敏度为200 MEV的置信度为200 MEV。为了实现这一目标，需要一个高度准确的模型预测，相对误差低于1E-4级。使用常规数值模型来分析最终的Katrin数据集的计算非常昂贵，或者需要近似以减少计算时间。我们减少计算要求的解决方案是训练神经网络，以了解预测的β-光谱及其对所有相关输入参数的依赖。这会导致计算加速约三个数量级，同时满足Katrin的严格准确性要求。
We present a fast and precise method to approximate the physics model of the Karlsruhe Tritium Neutrino (KATRIN) experiment using a neural network. KATRIN is designed to measure the effective electron anti-neutrino mass using the kinematics of beta-decay with a sensitivity of 200 meV at 90% confidence level. To achieve this goal, a highly accurate model prediction with relative errors below the 1e-4-level is required. Using the regular numerical model for the analysis of the final KATRIN dataset is computationally extremely costly or requires approximations to decrease the computation time. Our solution to reduce the computational requirements is to train a neural network to learn the predicted beta-spectrum and its dependence on all relevant input parameters. This results in a speed-up of the calculation by about three orders of magnitude, while meeting the stringent accuracy requirements of KATRIN.
