How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
复制标题
在实际搜索中，机器学习检测来自中子星的长瞬态引力波的效果如何？
作者：
Andrew L. Miller;P. Astone;S. D’Antonio;S. Frasca;G. Intini;I. La Rosa;P. Leaci;S. Mastrogiovanni;F. Muciaccia;Andonis Mitidis;C. Palomba;O. Piccinni;A. Singhal;B. Whiting;L. Rei
我们对卷积神经网络(CNN)探测孤立中子星的长持续时间的瞬时引力波信号的有效性进行了全面的研究。我们确定CNN对不同于训练集的信号形态是健壮的，并且它们不需要太多的训练注入/数据来保证良好的检测效率和低的虚警概率。事实上，我们只需要在单个150赫兹频段上训练一个CNN的信号/噪声地图；之后，CNN可以很好地区分任何频段的信号/噪声，尽管由于LIGO/Virgo中的非平稳噪声，CNN的效率和虚警概率不同。我们证明了我们可以通过在CNN的输出上选择最优阈值来控制CNN的虚警概率，这似乎是频率相关的。最后，我们将网络的检测效率与一种成熟的算法--广义频率霍夫算法(GFH)进行了比较，该算法将时间/频率平面上的曲线映射到与源的初始频率/自旋相关的平面上的直线。这些网络对GFH具有类似的灵敏度，但运行速度快了几个数量级，并且可以检测GFH对其盲目的信号。利用我们的分析结果，我们提出了将CNN应用到使用LIGO/Virgo数据的真实搜索中的策略，以克服我们将遇到的障碍，例如有限数量的训练数据。然后，我们使用我们的网络和策略来真正搜索GW170817的残骸，这是有史以来第一次应用机器学习方法来搜索来自孤立中子星的引力波信号。
We present a comprehensive study of the effectiveness of Convolution Neural Networks (CNNs) to detect long duration transient gravitational-wave signals lasting $O(hours-days)$ from isolated neutron stars. We determine that CNNs are robust towards signal morphologies that differ from the training set, and they do not require many training injections/data to guarantee good detection efficiency and low false alarm probability. In fact, we only need to train one CNN on signal/noise maps in a single 150 Hz band; afterwards, the CNN can distinguish signals/noise well in any band, though with different efficiencies and false alarm probabilities due to the non-stationary noise in LIGO/Virgo. We demonstrate that we can control the false alarm probability for the CNNs by selecting the optimal threshold on the outputs of the CNN, which appears to be frequency dependent. Finally we compare the detection efficiencies of the networks to a well-established algorithm, the Generalized FrequencyHough (GFH), which maps curves in the time/frequency plane to lines in a plane that relates to the initial frequency/spindown of the source. The networks have similar sensitivities to the GFH but are orders of magnitude faster to run and can detect signals to which the GFH is blind. Using the results of our analysis, we propose strategies to apply CNNs to a real search using LIGO/Virgo data to overcome the obstacles that we would encounter, such as a finite amount of training data. We then use our networks and strategies to run a real search for a remnant of GW170817, making this the first time ever that a machine learning method has been applied to search for a gravitational wave signal from an isolated neutron star.
影响因子：
2.1
作者：
Duncan A. Brown;K. Vahi;M. Taufer;Von Welch;E. Deelman;Lorena A. Barba;George K. Thiruvathukal
通讯作者：
Duncan A. Brown;K. Vahi;M. Taufer;Von Welch;E. Deelman;Lorena A. Barba;George K. Thiruvathukal
影响因子：
8.6
作者：
Gabbard, Hunter;Williams, Michael;Messenger, Chris
通讯作者：
Messenger, Chris
影响因子：
8.6
作者：
Abbott, B. P.;Abbott, R.;Zweizig, J.
通讯作者：
Zweizig, J.