Transient Classification in LIGO data using Difference Boosting Neural Network
Transient Classification in LIGO data using Difference Boosting Neural Network
复制标题
使用差分增强神经网络对 LIGO 数据进行瞬态分类
DOI：
10.1103/physrevd.95.104059
复制
发表时间：
2016
影响因子：
5
通讯作者：
N. S. Philip
中科院分区：
文献类型：
--
作者：
N. Mukund;S. Abraham;S. Kandhasamy;S. Mitra;N. S. Philip
对引力波探测器数据中的瞬态进行检测和分类对于有效搜索真实的天体物理事件和识别噪声源至关重要。我们提出了一种混合方法，使用监督和无监督机器学习技术对引力波数据中的短时瞬变进行分类。为了训练分类器，我们使用相对小波能量和通过对数据应用一维小波分解获得的相应熵。报告了经过训练的分类器对 9 类引力波瞬变模拟以及 LIGO 第六次科学运行硬件注入的预测精度。还提出了在高级 LIGO 数据的第一次观测运行中对一些已知类别的非天体物理信号的有针对性的搜索。使用最少的训练样本准确识别瞬态类的能力使得所提出的方法成为 LIGO 探测器表征以及搜索短持续时间引力波信号的有用工具。
Detection and classification of transients in data from gravitational wave detectors are crucial for efficient searches for true astrophysical events and identification of noise sources. We present a hybrid method for classification of short duration transients seen in gravitational wave data using both supervised and unsupervised machine learning techniques. To train the classifiers we use the relative wavelet energy and the corresponding entropy obtained by applying one-dimensional wavelet decomposition on the data. The prediction accuracy of the trained classifier on 9 simulated classes of gravitational wave transients and also LIGO's sixth science run hardware injections are reported. Targeted searches for a couple of known classes of non-astrophysical signals in the first observational run of Advanced LIGO data are also presented. The ability to accurately identify transient classes using minimal training samples makes the proposed method a useful tool for LIGO detector characterization as well as searches for short duration gravitational wave signals.