论文标题
词典学习：一种在带有丽莎银河噪声存在下检测二进制黑洞的新方法
Dictionary learning: a novel approach to detecting binary black holes in the presence of Galactic noise with LISA
论文作者
论文摘要
以银河系为基础的丽莎任务的主要目标之一，以百万白矮人二进制的灵感产生的噪音可能构成威胁：检测大规模的黑洞二进制合并。我们提出了一项新的研究，用于使用字典学习存在银河混乱噪声的合并波形。我们讨论了从$ 10^2 m _ {\ odot} $到$ 10^4 m _ {\ odot} $的二进制信号的限制。我们的方法证明，对于总质量大于$ \ sim 3 \ times 10^3 $ $ m _ {\ odot} $的二进制文件非常成功，直到保守的情况下为Redshift 3，在乐观的场景中最多可红移7.5。另外，如果信号噪声比率约为5或更高，则发现合并事件的良好波形重建。
The noise produced by the inspiral of millions of white dwarf binaries in the Milky Way may pose a threat to one of the main goals of the space-based LISA mission: the detection of massive black hole binary mergers. We present a novel study for reconstruction of merger waveforms in the presence of Galactic confusion noise using dictionary learning. We discuss the limitations of untangling signals from binaries with total mass from $10^2 M_{\odot}$ to $10^4 M_{\odot}$. Our method proves extremely successful for binaries with total mass greater than $\sim 3\times 10^3$ $ M_{\odot}$ up to redshift 3 in conservative scenarios, and up to redshift 7.5 in optimistic scenarios. In addition, consistently good waveform reconstruction of merger events is found if the signal-to-noise ratio is approximately 5 or greater.
