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Seeing with three sites

Seeing with three sites. Antony Searle LIGO Caltech. Three sites. In S6/VSR2 (July 09-) we have detectors with comparable (and astrophysically interesting) sensitivity at three widely separated sites New opportunities for robustness, pointing and thence multi-messenger astronomy.

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Seeing with three sites

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  1. Seeing with three sites Antony Searle LIGO Caltech Searle/Amaldi/2009/LIGO-G0900537

  2. Three sites • In S6/VSR2 (July 09-) we have detectors with comparable (and astrophysically interesting) sensitivity at three widely separated sites • New opportunities for robustness, pointing and thence multi-messenger astronomy Searle/Amaldi/2009/LIGO-G0900537

  3. New capabilities • Pointing • We have enough information to localize a source on the sky to within a few degrees • Internal consistency • Three detectors over-determine the unknown waveform • It is possible to reject glitches on the basis of poor consistency across the interferometers • To exploit these capabilities requires coherent analysis Searle/Amaldi/2009/LIGO-G0900537

  4. Bayesian framework • Idea • Construct explicit (but uninfomative) models for signals and glitches • Get back conditional probabilities for the model selection (detection) or pointing problem • Science • First outlined at last Amaldi meeting • Searle, Sutton, Tinto & Woan, Class. Quant. Grav. 25 (2008) • Searle, Sutton & Tinto, arXiv:0809.2809 (accepted, CQG) • Implementation • Core library in LAL • Integrated into Omega and CBC pipelines • Outputs parameter probability distributions (like MCMC) • Judicious choices of analytically marginalizable priors for some parameters keeps the analysis cheap • Fast enough to follow up data around one event per CPU per minute Searle/Amaldi/2009/LIGO-G0900537

  5. Process • Given an trigger template and time, we use the matched filter time series z[t] for 1 to 3 sites • (i.e. a coincident event found by a regular pipeline; this is a Bayesian followup) • Produce a conditional posterior probability distribution (“skymap”) for the source direction • Exactly the right data product for an optical followup • Skymaps for orthogonal templates can be combined • Cover arbitrary bursts with basis functions • Produce the conditional posterior probability of a noise burst (“glitch”) for each instrument individually Searle/Amaldi/2009/LIGO-G0900537

  6. Energy and probability skymaps WNB, SNR ~10, 25 ms, 100-300 Hz, white sim HLV SIMULATED DATA Searle/Amaldi/2009/LIGO-G0900537

  7. Model selection • The system has three states • Colored noise • Colored noise + signal • Colored noise + glitch • Coincident glitches • Each detector may independently experience a burst of noise • Uninformative glitch model: • Signals and glitches have the same distribution in any single instrument • Model selection is then a measure of the inter-site consistency • Though the signal and glitch models are very different, Bayesian analysis calibrates them both as probabilities • In the future, calibrate the glitch model on past observations, like the PSD Detector B Detector A Searle/Amaldi/2009/LIGO-G0900537

  8. Roadmap • Now: • Pointing in Omega and CBC followup pipelines • Uninformative glitch model in Omega pipeline • Omega online analysis providing pointing to LUMIN • Soon: • Pointing in LLOID • Uninformative glitch model in CBC followup pipeline and LLOID • Performance studies with SURF students • Future: • Calibrated glitch model Searle/Amaldi/2009/LIGO-G0900537

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