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Minutes of 2007-Nov-29 S5 QPipeline Review Teleconference


Shourov Chatterji, Jonah Kanner, Isabel Leonor, Dave Reitze

Minutes by Jonah Kanner.


Dave:  We were supposed to have familiarize ourselves with basics of search.
Shourov should pick up where he left off.

Shourov:  Did you read through chapters 3 and 5?

All:  We read much of it.

Jonah:  How does clustering work in Q-Pipeline?

Shourov:  Clustering is not a part of the standard q-pipeline.  S-G are minimum t-f uncertainty 
basis.  For a non-localized signal, we hope to represent it with the least number of tiles possible.  
Minimum t-f tiles lessens need for clustering.  Clustering algortihm is under development - not part of
S5 search.  We found that, for inspirals, can improve search if you do a good job of clustering.  Paper
to come out soon.  SG detection efficiency can be damaged by clustering algorithms.

On the other hand, current Q-pipeline does very well for non-localized signal.  The current problem is an OR 
problem - any one tile can trigger a detection.  For insprial, ~50% of energy can be seen in one tile.

Dave:  What wave-forms do you use for testing

Shourov:  Standard mdc's - we use a variety

Dave:  What is freq. band of search?

Shourov:  48 Hz - 2 kHz ???

Dave:  Is it true that noise is constant over freq. band?

Shourov:  No, but that "assumption" is never used in search.

Dave:  What is the meaning of the statement:  Q-Pipeline does not need to worry about noise when choosing basis?

Shourov:  Basis is not astrophysical.  Search "looks" for signals that are S-G AFTER whitening.  

Jonah:  Why does coherent-pipeline threshold "early"

Shourov:  We don't do it that way anymore.  In current algorithm, weighting is done in frequency domain, but summing 
is done after Q-transform.  

Shourov:  I'll go through thesis, chapter 5.  

Dave:  Why a bisquare window over a Hann window?

Shourov:  The bi-square window was simpler analyitically when finding the normalization factor.  Otherwise, they are 
very similar.

- on pg. 122, we see important restrictions to avoid aliasing.  Q > ~3.  Code issues errors if constraints are 
violated.  For higher values of Q, get narrower tiles, that can go closer to Nyquist frequency.  

Isabel:  Does mis-match metric still apply for bi-square window?

Shourov:  Becuase windows are similar, the metric is negligibly affected.  Simulations and Matlab experiments
show this to be so.  

-Normalization is chosen so mag (h) is recovered by Q transform coefficient
-Further, the normalization leads to expected coefficient = power spectral density for noise
-characteristic strain amplitude = hrss
- Can predict algorithm performance from first principles
- Statistics:  If you meet some requirements, you have enough statistics that noise tiles follow nice distributions.
You are able to add together tiles from different detectors, and know they share a distribution.  
- Standard normalized energy for Q-pipeline is defined.
- Normalized energy is simply related to significance and SNR

Dave:  I need to digest chapter 5 better - can do by next meeting.  Can we meet at GWDAW?
Maybe after dinner or go out to dinner.

All:  Yes.  Plan to meet at GWDAW.

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