We report the observation of gravitational waves from a binary-black-hole
coalescence during the first two weeks of LIGO's and Virgo's third observing
run. The signal was recorded on April 12, 2019 at 05:30:44 UTC with a network
signal-to-noise ratio of 19. The binary is different from observations during
the first two observing runs most notably due to its asymmetric masses: a ~30
solar mass black hole merged with a ~8 solar mass black hole companion. The
more massive black hole rotated with a dimensionless spin magnitude between
0.22 and 0.60 (90% probability). Asymmetric systems are predicted to emit
gravitational waves with stronger contributions from higher multipoles, and
indeed we find strong evidence for gravitational radiation beyond the leading
quadrupolar order in the observed signal. A suite of tests performed on
GW190412 indicates consistency with Einstein's general theory of relativity.
While the mass ratio of this system differs from all previous detections, we
show that it is consistent with the population model of stellar binary black
holes inferred from the first two observing runs.

观察到自第三轮 LIGO 和 Virgo 的前两周间的一个双黑洞并合事件中的引力波信号，认为这个事件证实了广义相对论，并且这个事件的质量比符合之前观测到的一类恒星双黑洞系统的种群模型。

GW190412: 非对称质量双黑洞合并观测

GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric  Masses

The detection of gravitational waves with LIGO and Virgo requires a detailed
understanding of the response of these instruments in the presence of
environmental and instrumental noise. Of particular interest is the study of
anomalous non-Gaussian noise transients known as glitches, since their high
occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational
wave signals. Therefore, successfully identifying and excising glitches is of
utmost importance to detect and characterize gravitational waves. In this
article, we present the first application of Deep Learning combined with
Transfer Learning for glitch classification, using real data from LIGO's first
discovery campaign labeled by Gravity Spy, showing that knowledge from
pre-trained models for real-world object recognition can be transferred for
classifying spectrograms of glitches. We demonstrate that this method enables
the optimal use of very deep convolutional neural networks for glitch
classification given small unbalanced training datasets, significantly reduces
the training time, and achieves state-of-the-art accuracy above 98.8%. Once
trained via transfer learning, we show that the networks can be truncated and
used as feature extractors for unsupervised clustering to automatically group
together new classes of glitches and anomalies. This novel capability is of
critical importance to identify and remove new types of glitches which will
occur as the LIGO/Virgo detectors gradually attain design sensitivity.

本文介绍了将深度学习与迁移学习结合应用于 LIGO 探测器数据中震荡信号辨识的方法，证明了将预训练模型用于谱图分类能够在小型数据集上显著减少训练时间，并且取得了 98.8% 以上的准确度，是一种能够自动识别和移除新型震荡信号和异常数据的创新能力。