On the Signal Processing Operations in LIGO signals
This article analyzes the data for the five gravitational wave (GW) events detected in Hanford(H1), Livingston(L1) and Virgo(V1) detectors by the LIGO collaboration. It is shown that GW170814, GW170817, GW151226 and GW170104 are very weak signals whose amplitude does not rise significantly during the GW event, and they are indistinguishable from non-stationary detector noise. LIGO software implements cross-correlation funcion(CCF) of H1/L1 signals with the template reference signal, in frequency domain, in a matched filter, using 32 second windows. It is shown that this matched filter misfires with high SNR/CCF peaks, even for very low-amplitude, short bursts of sine wave signals and additive white gaussian noise(AWGN), all the time. It is shown that this erratic behaviour of the matched filter, is due to the error in signal processing operations, such as lack of cyclic prefix necessary to account for circular convolution. It is also shown that normalized CCF method implemented in time domain using short windows, does not have false CCF peaks for sine wave and noise bursts. It is shown that the normalized CCF for GW151226 and GW170104, when correlating H1/L1 and template, is indistinguishable from correlating detector noise and the template. It is also shown that the normalized CCF for GW151226 and GW170104, when correlating H1/L1 and template, is indistinguishable from correlating H1/L1 and bogus chirp templates which are frequency modulated(FM) waveforms which differ significantly from ideal templates. Similar results are shown with LIGO matched filter, which misfires with high Signal to Noise Ratio(SNR) for bogus chirp templates.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
Gravitational wave astronomy is a rapidly growing field of modern astrophysics, with observations being made frequently by the LIGO detectors. Gravitational wave signals are often extremely weak and the data from the det…
AstronomyDeep LearningDenoisingDictionary LearningDeep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurat…
Gravitational Wave Detectionparameter estimationTime Series AnalysisDeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning
Gravitational wave (GW) detectors, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptible to background noise, which can obscur…
CPUDeep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurat…
Gravitational Wave Detectionparameter estimationTime SeriesTime Series AnalysisGravix: Active Learning for Gravitational Waves Classification Algorithms
This project explores the integration of Bayesian Optimization (BO) algorithms into a base machine learning model, specifically Convolutional Neural Networks (CNNs), for classifying gravitational waves among background n…
Active LearningBayesian OptimizationClassificationHyperparameter Optimization