Gravitational waves are ripples in the fabric of space-time that travel at the speed of light. The detection of gravitational waves by LIGO is a major breakthrough in the field of astronomy. Deep Learning has revolutionized many industries including health care, finance and education. Deep Learning techniques have also been explored for detection of gravitational waves to overcome the drawbacks of traditional matched filtering method. However, in several researches, the training phase of neural network is very time consuming and hardware devices with large memory are required for the task. In order to reduce the extensive amount of hardware resources and time required in training a neural network for detecting gravitational waves, we made SpecGrav. We use 2D Convolutional Neural Network and spectrograms of gravitational waves embedded in noise to detect gravitational waves from binary black hole merger and binary neutron star merger. The training phase of our neural network was of about just 19 minutes on a 2GB GPU.
翻译:LIGO对引力波的探测是天文学领域的一项重大突破。深层学习使许多行业,包括保健、财政和教育行业发生了革命性变革。还探索了深层学习技术,以探测引力波,克服传统匹配过滤方法的缺陷。然而,在一些研究中,神经网络的培训阶段耗时甚多,需要大量记忆的硬件设备来完成这项任务。为了减少培训神经网络以探测引力波所需的大量硬件资源和时间,我们制作了SpecGrav。我们使用了2D 进化神经网络和在噪音中嵌入的引力波的光谱,以探测二进制黑洞合并和二进制中子中子星合并产生的引力波。我们神经网络的培训阶段在2GBPU上仅19分钟。