论文标题
深度学习连续重力波：多个检测器和逼真的噪声
Deep-Learning Continuous Gravitational Waves: Multiple detectors and realistic noise
论文作者
论文摘要
宽参数空间搜索连续重力波的敏感性受到计算成本的限制。最近显示，深度神经网络（DNNS）可以直接在（单探测器）菌株数据上进行全天搜索，这可能提供了一种低计算成本搜索方法，该方法可能会导致更好的总体灵敏度。在这里，我们在两个方面对这项研究进行了扩展：（i）同时使用（模拟的）菌株数据，以及（ii）针对全天空搜索之外的定向培训（即\ \ \ \ \ \ \ \ \ \单个天空位置）搜索。对于$ t = 10^5 \，s $的数据时间潘，全套的两探测器DNN在低频（$ f = 20 \，hz $）下的敏感性（以振幅$ h_0 $）降低了$ 7 \％$少，在高频（$ f = 20 \，hz $）和$ 51 \，\％$较小的频率下（$ f = f = 1000 \，hz $）（hz $），（hz $ hz $）（hz $）（hz $ hz $）（比较）编织）。在指示情况下，与匹配过滤的范围相比，灵敏度差距约为$ 7-14 \％$ at $ f = 20 \，hz $，到$ 37-49 \％$ f = 1500 \，hz $。此外，我们评估了DNN在信号频率，旋转和天空位置上概括的能力，并测试了其对现实数据条件的稳健性，即数据中的间隙以及使用实际的Ligo检测器噪声。我们发现，DNN性能不会受到测试数据中差距的不利影响，也不会使用相对不受干扰的Ligo检测器数据而不是高斯噪声。但是，当使用更令人不安的LIGO带进行测试时，由于预期的错误警报的增加，DNN的检测性能会大大降级。
The sensitivity of wide-parameter-space searches for continuous gravitational waves is limited by computational cost. Recently it was shown that Deep Neural Networks (DNNs) can perform all-sky searches directly on (single-detector) strain data, potentially providing a low-computing-cost search method that could lead to a better overall sensitivity. Here we expand on this study in two respects: (i) using (simulated) strain data from two detectors simultaneously, and (ii) training for directed (i.e.\ single sky-position) searches in addition to all-sky searches. For a data timespan of $T = 10^5\, s$, the all-sky two-detector DNN is about $7\%$ less sensitive (in amplitude $h_0$) at low frequency ($f=20\,Hz$), and about $51\,\%$ less sensitive at high frequency ($f=1000\,Hz$) compared to fully-coherent matched-filtering (using WEAVE). In the directed case the sensitivity gap compared to matched-filtering ranges from about $7-14\%$ at $f=20\,Hz$ to about $37-49\%$ at $f=1500\,Hz$. Furthermore we assess the DNN's ability to generalize in signal frequency, spindown and sky-position, and we test its robustness to realistic data conditions, namely gaps in the data and using real LIGO detector noise. We find that the DNN performance is not adversely affected by gaps in the test data or by using a relatively undisturbed band of LIGO detector data instead of Gaussian noise. However, when using a more disturbed LIGO band for the tests, the DNN's detection performance is substantially degraded due to the increase in false alarms, as expected.
