Gravitational Wave (GW) burst detection algorithms typically rely on the hypothesis that the burst signal is "locally stationary", that is it changes slowly with frequency. Under this assumption, the signal can be decomposed into a small number of wavelets with constant frequency. This justifies the use of a family of sine-Gaussian templates in the Omega pipeline, one of the algorithms used in LIGO-Virgo burst searches. However there are plausible scenarios where the burst frequency evolves rapidly, such as in the merger phase of a binary black hole and/or neutron star coalescence. In those cases, the local stationarity of sine-Gaussians induces performance losses, due to the mismatch between the template and the actual signal. We propose an extension of the Omega pipeline based on chirplet-like templates. Chirplets incorporate an additional parameter, the chirp rate, to control the frequency variation. In this paper, we show that the Omega pipeline can easily be extended to include a chirplet template bank. We illustrate the method on a simulated data set, with a family of phenomenological binary black-hole coalescence waveforms embedded into Gaussian LIGO/Virgo-like noise. Chirplet-like templates result in an enhancement of the measured signal-to-noise ratio.
Modeling the Impulsive Noise Component and its Effect on the Operation of a Simple Coherent Network Algorithm for Unmodeled Gravitational Wave Bursts Detection
Multivariate Classification with Random Forests for Gravitational Wave Searches of Black Hole Binary Coalescence
对总质量为$\gtrsim25\,$ M $_\odot$的合并黑洞双星产生的引力波进行了匹配滤波。引力波探测器数据中的非高斯噪声爆发可以模拟短信号，并限制这些搜索的灵敏度。以前的搜索依赖于经验设计的统计数据，包括信噪比和基于信号的否决，以将引力波候选者与噪声候选者分开。我们报告使用来自监督机器学习算法的多变量候选人排名统计实现的灵敏度改进。我们应用随机森林的袋装决策树技术，在高质量的$\left( \gtrsim25\,\mathrm{M}_\odot \right)$参数空间中进行两次单独的搜索。对于一个搜索是敏感的引力波从螺旋，合并，和振铃（IMR）的二进制黑洞与$25\,$ M $_\odot$和$100\,$ M $_\odot$之间的总质量，我们发现灵敏的体积改善高达$70_{\pm 13}-109_{\pm 11}$ \%相比，以前使用的排名统计。对于振铃只搜索，这是敏感的引力波从所产生的扰动中间质量黑洞与质量大致之间的$10\,$ M $_\odot$和$600\,$ M $_\odot$，我们发现灵敏的体积改善高达$61_{\pm 4}-241_{\pm 12}$ \%相比，以前使用的排名统计。我们还报告了如何灵敏度的改善可以根据质量制度，质量比，和可用的数据质量信息而有所不同。最后，我们描述了用于调整和训练的随机森林分类器，可以推广到它的使用在其他搜索引力波的技…
Scalar Quasi-Normal Modes in Black Hole Gravitational Lensing
School of Astronomy and Space Science, Nanjing University（南京大学天文与空间科学学院）；Key Laboratory of Modern Astronomy and Astrophysics (Nanjing University), Ministry of Education（南京大学现代天文与天体物理教育部重点实验室）
Self-supervised reconstruction of transients in data from space-borne gravitational-wave detectors
Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所微重力实验室引力波实验中心）；Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS)（中国科学院大学太极引力波宇宙实验室（北京/杭州））
Probing soft signals of gravitational-wave memory with space-based interferometers
School of Physics, Nanjing University（南京大学物理学院）；School of Frontier Sciences, Nanjing University（南京大学前沿科学中心）；School of Astronomy and Space Sciences, University of Chinese Academy of Sciences (UCAS)（中国科学院大学天文与空间科学学院）
