Deep learning applied to data analysis of gravitational wave

• Main Authors: Meng-YouChen, 陳孟佑
• Other Authors: Yo, Hwei-Jang
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/pfk6tu

碩士 === 國立成功大學 === 物理學系 === 106 === This article uses analytical gravitational waveforms superposed with di erent types of noise to simulate the LIGO detection data. The LIGO uses the matched filtering technique to detect gravitational waves in a laser interferometer. Here, we use the machine learning method to decide whether there is a gravitational wave signal in a simulated waveform. Inspired by George and Huerta's paper, we use di erent convolution neuron network (CNN) architectures and hyperparameters to t our detector's data and study the behaviors for both shallow and deep neuron networks. In chapter 2, we introduce the CNN and some terminologies in machine learning.In chapter 3, we use a noisy sin-Gaussian function as a toy model to demonstrate the standard LIGO approach of whitening and matched ltering for detecting signals. Then we introduce the neuron network architecture and the initial data preparation in our machine learning scheme. In chapter 4, we demonstrate our result. We found our model shows a smoother sensitivity-versus-signal-to-noise-ratio curve for testing data with white noise than those with Gaussian noise. Our neuron network architecture consisted of four convolution layers and two fully connected layers yield much better results compared to other architectures. Then, we vary the dataset with randomly shifted waveform peaks to mimic real detection scenario, the behavior becomes worse and needs further study. In chapter 5, we summarize results and propose some improvements to our neuron network.