A Multiple Signal Classification Method for Directional Gravitational-wave Burst Search

Transcription

1 A Multiple Signal Classification Method for Directional Gravitational-wave Burst Search Junwei Cao LIGO Scientific Collaboration Research Group Tsinghua University, Beijing, China 3 rd Galileo - Xu Guangqi meeting October 12, 2011

2 Outline Introduction Real-time / low-latency GW burst search Motivation running before data Our method» Multiple signal classification (MUSIC)» Extension for GW DOA» Performance metrics» Performance evaluation Performance Comparison Conclusions

3 Our Group The LSC member group in China, including 3 faculty members and 3 students GW burst data analysis and computing infrastructure Also involved in LCGT, AIGO and ASTROD With close collaboration with MIT, Caltech and UWA This talk provides an introduction to one of our existing efforts on real-time / low latency GW burst search

4 LSC Burst Group Mission: Detection of unmodeled bursts of gravitational radiation Three dedicated pipelines:» Coherent Wave Burst (CWB) Pipeline S. Klimenko et al, Class.Quant.Grav.25:114029,2008» Kleine Welle for online detector characterization LIGO Document, LIGO-G Z, 2005» Omega Pipeline One group-crossed pipeline:» X-Pipeline for directional search

5 Real-time Search Real-time: between online and offline mode for largescale data analysis Online Monitoring Data Streams On-site Real-time Search Data Streams+ Data Production On-site+ Off-site Offline Analysis Data Production Off-site

6 Motivation Prompt E/M follow-up by LIGO s external collaborators Detect astronomy events earlier than traditional observation methods Increase the confidence of the GW candidate event Obtain more information about GW candidate event and its source: more accurate sky position, distance, Rapid detector characterization => New algorithms, methods and computing technology to enable faster real-time search, in particular, directional search

7 Challenges in AdvLIGO More potential IFOs: LCGT, AIGO,» More data streams flood into central location Larger Data Volume Cite from LIGO G

8 MUSIC The multiple signal classification (MUSIC) algorithm is one of the most popular subspace-based techniques for estimating the directions-of-arrival (DOAs) from linearly arrayed signal detectors. Dividing eigenspace to noise and signal subspaces, which are perpendicular to each other Giving arbitrary locations and arbitrary directional characteristics in a noisy environment of arbitrary covariance matrix, MUSIC is capable of giving asymptotically unbiased estimates of» Number of signals» DOA» Strengths and cross correslations among the directional waveforms» Polarizations» Strength of noise or interference

9 MUSIC Extensions MUSIC is widely used in periodic sine radio wave detection by antenna arrays in the plane condition. Several aspects are extended before applying MUSIC on GW burst search:» Using Spherical coordinates to extend from 2D to 3D» Using the concept of equal-phase to extend linearly arrayed detectors to generally placed detectors» Using linear transformation in time domain to extend the method to non-periodic signals

10 MUSIC Steps Collect data and form the covariance matrix S Calculate the Eigen structure of S in the matrix S0 Assuming that there is one signal in a relatively long period of time, get the eigenvectors of the noise subspace with the number of M-1 (M is the number of detectors) Calculate the Pmu(θ) and put it in a figure Find the peak of the signal Get DOA and other information of interest

11 Performance Evaluation

12 Experiment Design Self-generated Gaussian-moderated sinusoidal GW is injected into simulated LIGO data background. IIR filtering + MUSIC vs. Omega + Bayesian

13 Comparison Results The comparison result of MUSIC acting as the signal trigger versus Omega (Q transform). (Define A as the relative signal strength, which comes from the parameter of Factor of LogFile of injection part. A typical GW has a strength A~1) Parameters Low Limit of A Time Resolution Omega s 14s Time Consuming MUSIC s 3200s

14 Comparison Results The comparison result of MUSIC acting as DOA evaluator versus Bayesian. Parameters Low Limit of A Angel Resolution Bayesian rad 30s MUSIC 1000 Complicated 4.2s Time Consuming

15 Bayesian Results Bayesian skymap A=100

16 MUSIC Results

17 Conclusion Current burst real-time low latency search is successful, but not perfect Advanced computing technology and new signal processing methods can significantly boost real-time multi-messenger astronomy Multiple signal classification have potential to provide faster direction estimation, though current SNR ratio resolution and time resolution are not satisfactory