GravStat 2005: Finn

Sam Finn's talk on statistical issues for GW.

Inference requires assumptions:
Examples : Bound the rate of coalescing compact binaries as a function of mass : what can we surmise about binary synthesis and evolution.
: Bound amplitude of gravitational waves from a pulsar: how can we choose among models for nuclear EOS.
: Detect burst associated with GRB : what can we say about the central engine.

Regimes of interest
Today : weak (low snr) & rare (serendipitous)
Tomorrow: ground-based : strong at moderate rate
space-based : strong and (over-)abundant - confusion

We observe signals not sources
Stochastic: continuous, possibly modulated
Periodic: known frequency, unknown frequency
Bursts: characterized by eg waveform, (time-)frequency spectrum, energy spectrum, amplitude, bandwidth, duration

Multi-layered data analysis:
Data conditioning: removing artifacts - regression, vetoes, whitening (flat-field),...
Signal ID and characterization: amplitudes, durations, spectra, locations, bandwidths, waveforms, rates. classification - signals of same tyoe
Interpretation of signals as sources: source eg mass, spin, differential rotation; process - hypernova or binary coalescence, neutrino opacity etc.; population - mass function, spatial distribution, luminosity function, evolution,...

Where are we now ?
Focussed on signal ID and characterization - haven't seriously tackled interpretation.
Statistical tools used for ID and characterization:
confidence interval construction on rates (bursts, stochastic)
extreme value statistics (inspiral)
Bayesian credible sets (periodic signals)

Open questions:
Do we/did we see strong evidence for gravitational waves. Quantify strong.
How do we incorporate prior knowledge or experience without closing our minds to discovery ?
When and how may we "re-analyze" data ? ie carry out an improved analysis, incorporate new info.
How do we go about classifying signals ?
How do we select among physical models for phenomena ?
How do we infer population properties for incomplete and biased samples ?
How do we compare and combine different experiments.