Real time gravitational wave burst search for multi messenger astronomy
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Junwei Cao LIGO Scientific Collaboration Research Group Tsinghua University, Beijing, China November 4, 2010. Real-time Gravitational-wave Burst Search for Multi-messenger Astronomy. Outline. Introduction LSC Burst Group What’s Real-time Search Motivation Our method and pipeline

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Real-time Gravitational-wave Burst Search for Multi-messenger Astronomy

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Real time gravitational wave burst search for multi messenger astronomy

Junwei Cao

LIGO Scientific Collaboration Research Group

Tsinghua University, Beijing, China

November 4, 2010

Real-time Gravitational-wave Burst Search for Multi-messenger Astronomy


Outline

Outline

  • Introduction

  • LSC Burst Group

  • What’s Real-time Search

  • Motivation

  • Our method and pipeline

    • Decentralized GWB data processing

    • DMT monitor on pipeline trigger for glitch study

    • GPU acceleration in GWB data processing

    • Pattern recognition method for veto analysis

  • Conclusion


Our group

Our Group

  • The LSC member group in China, including 3 faculty members and 5 students

  • Expertise lies in GW 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 our several existing efforts on GW data analysis


Lsc burst group

LSC Burst Group

  • Mission: Detection of unmodeled bursts of gravitational radiation

  • Three dedicated pipelines:

    • Omega Pipeline

      • https://geco.phys.columbia.edu/omega

    • Coherent Wave Burst (CWB) Pipeline

      • S. Klimenko et al, Class.Quant.Grav.25:114029,2008

    • Kleine Welle for online detector characterization

      • LIGO Document, LIGO-G050158-00-Z, 2005

  • One group-crossed pipeline (mainly Burst Group):

    • X-Pipeline for directional search

      • https://geco.phys.columbia.edu/xpipeline


Real time search

Real-time Search

  • Real-time: between online and offline mode for large-scale 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


Motivation

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, …


Motivation1

Motivation

Blue: SNR>5

Green: SNR>10

Red: SNR>20

Star: Loudest trigger

Rapid detector characterization


Burst search in s6

Burst Search in S6

Nearly Real-time low latency analysis for the first time


Burst search in s61

Burst Search in S6

  • But low latency’s price is low precision

    • Potentially increase false alarm rate at the expense of fully utilization of AUX channels info

    • Less accurate sky position and other info of GW candidate events

  • Multi-messenger astronomy can be performed better in Burst search

    • Directional search is not merged into current real-time burst search


Challenges in advligo era

Challenges in AdvLIGO Era

  • More potential IFOs: LCGT, AIGO, …

    • More data streams flood into central location

  • Larger Data Volume

Cite from LIGO-G0900008


Our pipeline

Our Pipeline


Key features

Key Features

Decentralized GWB data processing

DMT monitor on pipeline trigger for glitch study

GPU acceleration in GWB data processing

Pattern recognition for veta analysis


Distributed pipeline

Distributed Pipeline

  • Decentralized GWB data processing

    • For example, Omega Pipeline’s 3 main functions

      • Single site trigger generation

      • Coincidence check between detectors

      • Followup analysis

    • In current Burst search, all 3 functions are in CIT, waits for all 3 sites data ready, then run

    • In decentralized GWB data processing, trigger generation function runs on single site, only the triggers transferred to CIT, coincidence check will decide whether to get h(t) data and AUX data. This can

      • Lowers down CIT’s IO throughput

      • Reduce latency


Omegamon

OmegaMon

Trigger Rate on L1

Trigger Rate on H1

Triggers’ scatter plot on L1

Triggers’ scatter plot on H1

  • DMT monitor

    • An example, OmegaMon, Omega Pipeline’s DMT monitor


Gpu acceleration

GPU Acceleration

  • Time consuming algorithms in burst search: such as Omega’s followup function, it can cost a couple of minutes on a 64 seconds block of data, which cannot meet real-time requirement

  • Condor job fail problem: Both Omega and CWB need background estimation, which needs run hundreds of condor jobs on cluster. But condor jobs could fail due to various reasons. If GPU acceleration can get hundreds of condor jobs run on only one PC, then this problem would not exist.


Gpu acceleration1

GPU Acceleration

A case: GPU acceleration on IIR (Infinite impulse response) filtering

For a predicted inspiral waveform, it can be decomposed into a series of constant frequency decaying sinusoids. Each sinusoid is used to define a single IIR filter. The coherent addition of the output of the set of IIR filters recovers predicted waveforms with near optimal signal to noise ratio. Since among IIR filters , the independence are significant, which is suitable for GPU acceleration parallel process.


Gpu acceleration2

GPU Acceleration

  • 50-fold speedup over the traditional CPU implementation


Gpu acceleration3

GPU Acceleration

  • We can currently handle 3000 templates in real time.


Veto analysis

Veto Analysis

  • Pattern recognition in trigger veto

    • To identify background events during Burst data analysis, one common way is to use information from multiple auxiliary channels. Traditional veto method is to analyze the coincidence between GW trigger and every AUX trigger independently. If there exits a coincident AUX trigger similar with the GW trigger in certain kind, the GW trigger is believed to be generated by the corresponding AUX channel, not by gravitational wave.


Veto analysis1

Veto Analysis

  • Pattern recognition in trigger veto

  • Compared with traditional single–channel fixed-window approach, pattern recognition provides another vision. It utilizes hundreds of auxiliary channels’ information at the same time and classify glitches more efficiently.

  • Take SVM (Support Vector Machine) method as an example. In general, SVM works by finding a maximum-margin hyperplane to separate samples in a transformed space, defined by a kernel function.


Veto approach

Veto Approach

  • The veto process can be considered as a classification problem of instrument status:

    • The input of the classifier is the combination of properties of all coincident AUX/ENV triggers at the time ti, assuming there is a GC trigger at the time ti.

    • The output of the classifier is whether the instrument is fault or not.

      • If yes, the GC trigger is a glitch;

      • If not, the GC trigger is a GW signal

No signal and

no glitch either!

No instrument faults!

perfect for training

the classifier

We want to know

if this is a signal

or a glitch

This is definitely

a GW signal

GC1100

AUX10100

AUXn0010

ENV10010

ENVm0100


Veto analysis2

Veto Analysis


Veto analysis3

Veto Analysis

Efficiency: fraction (%) of GW triggers rejected.

Dead-time: fraction (%) of live-time lost due to veto application.

  • Pattern recognition in trigger veto

    • Comparison between SVM and traditional method on S4 L1 data set


Conclusion

Conclusion

Current Burst real-time low latency search is successful, but not perfect

To be ready for AdvLIGO, burst search infrastructure should evolve

Advanced computing technology and method can significantly boost real-time multi-messenger astronomy


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