Giovani Prodi introductory remarks
1. Blind vs. explanatory analysis - use of priors, exploit F & B methods.
2. Noise background estimation - confidence required ? statistical uncertainties on background ? discriminate correlated noise from target gw signals.
3. Can we rely on statistics alone for gw searches ? what is added value of a posteriori physical information ? role of systematic uncertainties ?
4. Multiple observations - comparison & synthesis of different observations, control False Discovery Rate in multiple trials.
5. Validation of results of a network or a detector - independent analysis procedures, injections of s/w signals.
Comments by Daisuke Tatsumi
On 1) blind analysis ideal for 1st detection. Need full detector noise simulation (including burst noise).
On 2) most important is systematics
On 5) joint search program looks good.
Questions from Peter Shawhan
How much should astrophysical assumptions feed into Bayesian analysis of data ?
How much should assumptions on rates influence design of analysis procedures ?
General discussion
Should posteriors from previous experiments be used as priors ? This is done for pulsars. For LIGO the sensitivities are improving so rapidly that posteriors for previous runs are effectively flat.
What cuts do you apply on background ? This might depend on how strong a source you were looking for.
Types of noise: white, harmonic, burst, auto-regressive (1/f). If all these matter then very tough statistical problem.
Can create models looking for ill-defined targets eg Larry Bretthorst's work on searching for radar profiles of planes for US Army - priors for known planes plus low prior for a new type of plane which is very poorly defined. Unknown type of plane will not be detected till it it closer but it can be detected.
There are techniques to build likelihood from multiple time series that have linear correlations.
Critical test is: does signal/noise ratio improve as sensitivity improves.
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