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Exploring Upper Limits on Binary Systems using Monte Carlo Analysis in GEO02 Code

This project involves developing and testing code for analyzing binary systems in the GEO02 library, with focus on waveform families, code organization, and setting upper limits for NS-NS and BH-BH binaries. The analysis includes template generation, signal injections, simulations, and candidate selection from interferometer data. By leveraging Monte Carlo simulations, this research aims to estimate sensitivity and event rates, identify noise glitches, and improve error margins. Despite challenges in sensitivity at low frequencies, the project is paving the way for future enhancements like hierarchical search and intelligent template algorithms for efficient signal processing.

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Exploring Upper Limits on Binary Systems using Monte Carlo Analysis in GEO02 Code

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  1. GEO Binary Inspiral Search Analysis GEO02

  2. Template generation, placement and Monte Carlo codes • Three LAL (inspiral, bank, noisemodels) and one non-LAL (but LAL-standard-complaint) geoinspiralsearch, libraries • 10,000 lines of code • 150 pages of documentation GEO02

  3. Waveform families • Five approximants (three different post-Newtonian families, P-approximants, effective one-body approach) • Seven different post-Newtonian orders GEO02

  4. Code Organisation Waveform generation Codes Template Bank codes Signal Injection, Monte Carlo simulations MPI Shell Master Slave Single instruction multiple data Database GEO02

  5. Upper limitstwo goals • Set upper limits on NS-NS binaries in the range 1-3 solar masses for individual components • Explore setting upper limits on BH-BH binaries in the range 3-20 solar masses • What issues are facing us with regard to setting upper limits on BH-BH binaries GEO02

  6. IUL Plan • Confirm the minimal match over the template bank for binaries in the mass range of interest (done). • Monte Carlo a binary inspiral search through simulated Gaussian data from a single interferometer to determine crude thresholds for a search through real data (done). • Run the inspiral search codes on real (playground) data to estimate the difference between the rate in real data versus that in simulated Gaussian noise (ongoing). • [20 February 2002] Use the results from the run through playground data to develop a set of veto based on the tools currently at our disposal: • In the process, identify classes of noise glitch and tools which are useful for this classification process. • [7 February 2002] Do simulated signal injections into the playground data to estimate the sensitivity of the interferometers to binary neutron star inspiral in the galaxy. • [28 February 2002] Run the search codes on the data to produce a list of candidates from each of the interferometers: H1, L1, H2 and GEO. • [10 March 2002] Inject simulated signals from a Galactic population of binaries into the data stream to determine the efficiency of the search method. • [10 March 2002] Produce a list of candidates from each of the interferometers, combine and determine the loudest (in the sense of multi-interferometer statistic) surviving event or events depending on the statistic to be used for the upper limit. • [10 March 2002] Determine the upper-limit on event rate using the loudest event (or other) method. What's the answer? GEO02

  7. A Monte Carlo Simulationtesting the code GEO02

  8. A Monte Carlo Simulationparameter space GEO02

  9. Monte Carlo Simulationoverlaps GEO02

  10. Monte Carlo Simulationsignal-to-noise ratios GEO02

  11. Monte Carlo Simulationoverlap histogram GEO02

  12. Monte Carlo Simulationsnr - cumulative GEO02

  13. Monte Carlo Simulationsnr histogram GEO02

  14. Monte Carlo Simulationjob statistics GEO02

  15. Conclusions • Codes are ready and tested • Initial explorations have begun • Some results expected by the March LSC • But sensitivity not good enough at low frequencies to set any meaningful upper-limits (may be just about as good, but certainly not better than 40 m analysis) GEO02

  16. Future • Write a MPI shell for hierarchical search • Include (intelligent) template storing algorithms to minimize signal generation costs (e.g. one-parameter family of mother templates that depend only on the total mass rather than a two-parameter family) • Include spin effects (Alberto et al) in our templates GEO02

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