论文标题
通过机器学习对直接暗物质搜索数据的快速而灵活的分析
Fast and Flexible Analysis of Direct Dark Matter Search Data with Machine Learning
论文作者
论文摘要
我们使用来自大型地下氙（LUX）暗物质实验的数据将机器学习与配置文件可能拟合过程相结合的结果提供了结果。与先前的方法相比，这种方法表明，计算时间的缩短了30倍，而实际数据上的性能降低了。我们建立了其灵活性，可以通过使用和不使用没有位置校正的脉冲区域来实现均等性能，从而捕获变量之间的非线性相关性（例如，在光和电荷信号中涂抹）。它的效率和可伸缩性进一步可以使用其他变量搜索暗物质，而无需显着计算负担。我们通过在更传统的输入（例如光和电荷信号强度）以及更传统的输入以及更传统的输入旁边包含光信号脉冲形状变量来证明这一点。可以通过未来的暗物质实验来利用此技术来利用其他信息，减少信号搜索和仿真所需的计算资源，并使拟合中的物理滋扰参数包括在内。
We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture non-linear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs such as light and charge signal strengths. This technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable.
