标题： 评估非高斯噪声对寻找连续引力波的卷积神经网络的影响 显示英文标题

标题： Assessing the impact of non-Gaussian noise on convolutional neural networks that search for continuous gravitational waves

摘要： 我们提出了一种卷积神经网络，能够从$\sim 1$年模拟数据中搜索连续引力波，这些数据受到非平稳、窄带干扰（即线状噪声）的影响。我们的网络已经学会将输入的应变数据分类为四类：（1）只有高斯噪声；（2）高斯噪声中的天体物理信号注入；（3）嵌入高斯噪声中的线状噪声；（4）同时受到高斯噪声和线状噪声污染的天体物理信号。 在我们的算法中，不同频率被独立处理；因此，我们的网络对均匀间隔的线状噪声（即梳状结构）具有鲁棒性，并且只需考虑完全正弦的线状噪声。我们发现，我们的神经网络能够以高精度区分天体物理信号和线状噪声。 在一个没有线状噪声的频段内，我们的网络灵敏度深度约为$\mathcal{D}^{95\%} \simeq 43.9$，假警报概率为$\sim 0.5\%$；而在存在线状噪声的情况下，当线状噪声幅度为$h_0^\mathrm{line}/\sqrt{S_\mathrm{n}(f_k)} = 1.0$时，我们能够保持假警报概率为$\sim 10\%$并达到$\mathcal{D}^\mathrm{95\%} \simeq 3.62$的灵敏度。 我们评估了该方法的计算成本为$O(10^{19})$浮点运算，并将其与标准全天空搜索的成本进行了比较，忽略覆盖参数空间的差异。结果显示，我们的方法比标准搜索方法高效一个或两个数量级。 尽管我们的神经网络使用当前设施（一块 GTX1080Ti 显卡）大约需要$O(10^8)$秒来部署，但我们预计通过利用更多改进后的显卡，可以将其减少到可接受的水平。 显示英文摘要

摘要： We present a convolutional neural network that is capable of searching for continuous gravitational waves, quasi-monochromatic, persistent signals arising from asymmetrically rotating neutron stars, in $\sim 1$ year of simulated data that is plagued by non-stationary, narrow-band disturbances, i.e., lines. Our network has learned to classify the input strain data into four categories: (1) only Gaussian noise, (2) an astrophysical signal injected into Gaussian noise, (3) a line embedded in Gaussian noise, and (4) an astrophysical signal contaminated by both Gaussian noise and line noise. In our algorithm, different frequencies are treated independently; therefore, our network is robust against sets of evenly-spaced lines, i.e., combs, and we only need to consider perfectly sinusoidal line in this work. We find that our neural network can distinguish between astrophysical signals and lines with high accuracy. In a frequency band without line noise, the sensitivity depth of our network is about $\mathcal{D}^{95\%} \simeq 43.9$ with a false alarm probability of $\sim 0.5\%$, while in the presence of line noise, we can maintain a false alarm probability of $\sim 10\%$ and achieve $\mathcal{D}^\mathrm{95\%} \simeq 3.62$ when the line noise amplitude is $h_0^\mathrm{line}/\sqrt{S_\mathrm{n}(f_k)} = 1.0$. We evaluate the computational cost of our method to be $O(10^{19})$ floating point operations, and compare it to those from standard all-sky searches, putting aside differences between covered parameter spaces. Our results show that our method is more efficient by one or two orders of magnitude than standard searches. Although our neural network takes about $O(10^8)$ sec to employ using our current facilities (a single GPU of GTX1080Ti), we expect that it can be reduced to an acceptable level by utilizing a larger number of improved GPUs.