Status of stochastic background’s joint data analysis by Virgo and INFN resonant bars

1. Status of stochastic background’s joint data analysis by Virgo and INFN resonant bars G. Cella (INFN Pisa) For Auriga-ROG-Virgo collaborations Prepared for the ILIAS-GWA Meeting

2. The collaboration • 3 groups involved: • AURIGA • ROG (Nautilus + Explorer) • Virgo • Common frequency range: 850-950 Hz • Bandwidth: 5-30 Hz

3. The project • Joint data analysis experiment between Virgo, Auriga, Explorer, Nautilus • Detection of stochastic background signal • Software injection of simulated data • Analysis of real noise: 4 hours of simultaneous data for the first phase Purpose: • Exchange and preprocessing of data • Signal simulation and injection • Gaussian, isotropic & non polarized model • Flat energy density power spectrum • Detection • Standard cross correlation analysis Steps:

4. Data exchange • Purpose: test of independent procedures of data generation and exchange. • Each member of the collaboration was asked to provide a small stream of data (4 hours) which was stored in a common repository in Bologna (CNAF). Data content: • A continuous time series with the reconstructed strain observed by the detector • A minimal data quality channel, which will consist in a simple boolean flag for each value of the time series (valid/not valid). • Valid means that the data can be safely used for processing. Not valid means that the data cannot be trusted for reasons that does not have to be specified (independent vetoing procedure).

5. Signal injection • A multiple stream of simulated stochastic background signals was generated, taking into account the real orientation and position of each detector. SNR=0,5,10,20. • The signal is the one appropriate for a stationary, Gaussian and unpolarized stochastic background with a flat spectrum in The problem to solve: generate N continuous streams of Gaussian data with a known cross correlation array: Normalization Overlap Reduction Function

6. Signal simulation: some details on the approach • Generate streams with a flat spectrum in h but with the correct coherence: • Square root of is obtained using SVD • Vectorial filtering is implemented with Overlap and Add • We know what is the correct padding to use in the flat case: • Filter all the streams to obtain the correct power spectrum (in our case, )

7. Simulated data: a cross check • Overlap reduction function reconstruction from simulated data

8. Detection (with standard cross correlation analysis) Minimum detectable energy density:

9. Expected outcomes • Test of data analysis software • Test of syncronization issues • Research on methods to deal with noisy periods and dead times No ambitions of finding upper limits, at least in this first methodological study. But:

10. Future steps • First phase of detection should be completed during this month • Finalization of first phase foreseen for november • Second phase: upper limit with longer stretches of data?