CBC R&D presentation
Technicalities: 1 slide
code versions
Code implementations
PE runs
Vetting LVT170729: 1 slide
What about PyCBC and GstLAL? If a significant fraction of the PE samples are consistent with their significance estimate (IFAR~1 yr) then we will know that for events like LVT170729 the discrepancy in significance that we observe is to be expected.
SImilar results for PE samples from the IMRPhenomHM run
~30% of LVT170729 LALInference PE samples when injected on chunk 19 are found by cWB with higher significance than the original event (i.e. IFAR=56 yrs)
Consistency test: 2 slides
LVT170729: plot
Extra pixel
Minor inconsistency in the merger
LVT170729: summary of PE runs so far
Issues
Why a burst pipeline is finding a BBH with higher significance than MF pipelines?
"Boring" explanation: currently, cWB is more optimized than MF pipelines wrt heavier BBH systems
Interesting explanations: deviations from GR, features not included in current template banks, such as HM, precession or eccentricity, etc.
the first event for which our confidence in detection is based almost entirely on cWB
we should make as strong a case as we can in terms of testing consistency with a real signal
Introduction: 3 slides
Basic description of what we have done
Confidence belts: Departure of the cWB point estimate (red curve) outside of the corresponding confidence belts
gives us a conservative measure of the statistical agreement between the burst waveform and the PE samples
Method: we compare the cWB point estimate waveform for a given GW/LVT event with a distribution of its PE posterior samples injected on O2 data and reconstructed by cWB.
