神经网络辅助的毛刺-突发判别与毛刺分类
Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
摘要：
我们调查的潜在的神经网络为基础的分类区分引力波爆发（GWB）的一个给定的典型的家庭（如核心崩溃超新星波形）从典型的瞬态仪器文物（毛刺），在一个单一的检测器的数据。为了提供一个概念证明，我们使用了H。Dimmelmeier和同事，以及由P. Saulson和同事维护的激光干涉仪引力波天文台（LIGO）数据中观察到的毛刺数据库，以构建具有不同信噪比（SNR）的加性（高斯和复合高斯）噪声中（加窗）瞬态波形（毛刺和突发）的数据集。主成分分析（PCA）的下一个实现减少数据的维数，产生的结果一致，并在文献中扩展。然后，一个多层感知器训练的数据子集上的反向传播算法（MLP-BP），并用于分类的瞬态毛刺或突发。一个自组织映射（SOM）架构，最后使用分类毛刺。根据相关的真值表来衡量毛刺/突发鉴别和毛刺分类能力。初步结果表明，该方法是有效的和强大的整个SNR范围内的实际利益。前景应用涉及GWB的分布式（网络，多传感器）检测，其中可以引入单节点级别的一些智能，以及仪器诊断/优化，其中可以识别，分类并有望追溯到其入口点的虚假瞬态
英文摘要：
We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored.In order to provide a proof of concept,we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-tonoise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where someintelligenceat the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry points
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
