利用粒子群优化和交叉验证解析LISA数据中的银河双星
Resolving Galactic binaries in LISA data using particle swarm optimization and cross-validation
摘要：
空间引力波探测器LISA预计将观测银河系中大量主要由白矮星组成的致密天体双星的信号。从这个群体中分辨个体来源与其自身产生的混淆噪声构成了一个主要的数据分析问题。我们提出了一种迭代源估计和减法方法来解决这个问题的基础上使用粒子群优化（PSO）。除了粒子群算法，该方法的一个新特点是交叉验证的来源估计从相同的数据使用不同的信号参数搜索范围。这被发现大大减少污染的虚假来源，并可能被证明是一个有用的除了任何多源分辨率方法。应用到最近的模拟数据的挑战，该方法是能够找到$O(10^4)$银河双星的信号频率范围内的$[0.1,15]$兆赫，并为频率$\gtrsim 4$兆赫，减去估计信号的仪器噪声水平后，减少了残余数据。
英文摘要：
The space-based gravitational wave (GW) detector LISA is expected to observe signals from a large population of compact object binaries, comprised predominantly of white dwarfs, in the Milky Way. Resolving individual sources from this population against its self-generated confusion noise poses a major data analysis problem. We present an iterative source estimation and subtraction method to address this problem based on the use of particle swarm optimization (PSO). In addition to PSO, a novel feature of the method is the cross-validation of sources estimated from the same data using different signal parameter search ranges. This is found to greatly reduce contamination by spurious sources and may prove to be a useful addition to any multi-source resolution method. Applied to a recent mock data challenge, the method is able to find $O(10^4)$ Galactic binaries across a signal frequency range of $[0.1,15]$ mHz, and, for frequency $\gtrsim 4$ mHz, reduces the residual data after subtracting out estimated signals to the instrumental noise level.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
