标题： 一种用于直接检测轴子状粒子域壁的机器学习算法 显示英文标题

标题： A machine learning algorithm for direct detection of axion-like particle domain walls

摘要： 基于全球同步原子磁力计阵列的光学磁力计全球网络（GNOME）针对暗物质的某些形式开展了基于其时空特征信号的实验性搜寻。 本文描述了一项通过梯度耦合寻找类轴子粒子（ALP）与质子自旋相互作用的工作，这种相互作用作为局部高密度暗物质对象（如畴壁）的标志。 在这项工作中，提出了一种结合机器学习的随机优化方法，用于基于GNOME数据搜寻ALP畴壁。 通过二分类验证了该方法的有效性和可靠性。 此外，还介绍了这种新分析方法对ALP畴壁穿越事件的预期灵敏度。 显示英文摘要

摘要： The Global Network of Optical Magnetometers for Exotic physics searches (GNOME) conducts an experimental search for certain forms of dark matter based on their spatiotemporal signatures imprinted on a global array of synchronized atomic magnetometers. The experiment described here looks for a gradient coupling of axion-like particles (ALPs) with proton spins as a signature of locally dense dark matter objects such as domain walls. In this work, stochastic optimization with machine learning is proposed for use in a search for ALP domain walls based on GNOME data. The validity and reliability of this method were verified using binary classification. The projected sensitivity of this new analysis method for ALP domain-wall crossing events is presented.