1. Topology comparison RSE vs. SAGNAC using GWINC S. Chelkowski, H. Müller-Ebhardt, S. Hild 21/09/2009 S. ChelkowskiSlide 1ET Workshop, Erice, 10/2009

2. Overview  Intro & Motivation  Noise coupling differences & similarities  Displacement noise in a 2- & 4-mirror cavity  Beam geometry influence on thermal noise  GWINC model  Results  Conclusion S. ChelkowskiET Workshop, Erice, 10/2009Slide 2

3. ET note on QND review S. ChelkowskiET Workshop, Erice, 10/2009Slide 3 Conclusion Sagnac Topology seems to give the best quantum noise performance

4. More realistic comparison of Sagnac and RSE topology S. ChelkowskiET Workshop, Erice, 10/2009Slide 4 RSESAGNAC

5. Noise coupling – Sagnac compared to RSE  Quantum noise characteristic change  How do various displacement noises couple more due to four mirror cavities, e.g. TN, Seismic noise, etc...  Beam geometry changes and influences TN further  Higher losses in four mirror arm cavity  Etc... S. ChelkowskiET Workshop, Erice, 10/2009Slide 5

6. Coupling of displacement noises S. ChelkowskiET Workshop, Erice, 10/2009Slide 6 Cavity DesignSignalNoise For two mirrors of the 4 mirror cavity: Signal: Noise:

7. Beam geometry changes affect the TN  Beam geometry on mirror surface depends on incidence angle Initial beam shape defined by: changes into: S. ChelkowskiET Workshop, Erice, 10/2009Slide 7 Take clipping loss into account in the following analysis Note Top view Front view

8. Beam geometry in the 4 mirror cavity  Mirror sizes used: r=31cm  Round beam with w=12cm has clipping loss of 1ppm S. ChelkowskiET Workshop, Erice, 10/2009Slide 8 Beam size on one of the four arm cavity mirrors Resulting Beam w x = 12cm & w y = 10cm

9. Consequences for the thermal noise S. ChelkowskiET Workshop, Erice, 10/2009Slide 9 Coating Brownian thermal noise: PSD: CBTN adaptation to elliptical beam: with Beam intensity distribution: Bessel function of 0. order with, we find & Analytically not solvable! Numerical integration

10. Result for CBTN S. ChelkowskiET Workshop, Erice, 10/2009Slide 10 CBTN for elliptical beam shape using the minor beam width The same was done for the Substrate Brownian thermal noise! with CBTN correction factor

11. Result for SBTN S. ChelkowskiET Workshop, Erice, 10/2009Slide 11 CBTN for elliptical beam shape using the minor beam width with SBTN correction factor

12. Result for our specific case S. ChelkowskiET Workshop, Erice, 10/2009Slide 12

13. Current model status  Single detector  L = 10km  Underground  Seismic noise reduced  Gravity gradient noise reduced  Adapted thermal noises & suspension model  Optimisation parameters  Input laser power P  SR, ITM & PRM transmissivity  Tuning of SRM  Readout quadrature with homodyne detector S. ChelkowskiET Workshop, Erice, 10/2009Slide 13

14. Things to keep in mind  This is a topology analysis and there is no intension to create a new baseline sensitivity curve  Following sensitivities are lower than the ones presented on the ET webpage  No magical factor used e.g. for the grav. gradient noise  No squeezing used  No cryogenic techniques  Nevertheless the following comparison is valid  The model can be changed later and a higher performing configuration can be found via optimisation S. ChelkowskiET Workshop, Erice, 10/2009Slide 14

15. Figure of merit and optimisation  BHBH and NSNS inspiral ranges  Give parameters of standard candles.  BHBH: Mass each 30Msol, Distance 10Mpc  NSNS: Mass each 1.4Msol, Distance 23Mpc  5D parameter space is scanned automatically for the optimal configuration with an adaptive resolution S. ChelkowskiET Workshop, Erice, 10/2009Slide 15

16. RSE topology results – tuned configuration S. ChelkowskiET Workshop, Erice, 10/2009Slide 16 BHBH: 9736MpcNSNS: 871Mpc

17. RSE topology results – 5D - auto optimisation S. ChelkowskiET Workshop, Erice, 10/2009Slide 17 BHBH optimisation NSNS optimisation BHBH: 11776Mpc NSNS: 949Mpc

18. SAGNAC topology results – 5D - auto optimisation S. ChelkowskiET Workshop, Erice, 10/2009Slide 18 BHBH: 14836MpcNSNS: 1282Mpc

19. Comparison of results – BHBH optimisation - I S. ChelkowskiET Workshop, Erice, 10/2009Slide 19 RSE – tuned SRSAGNAC-optimised BHBH inspiral range for Sagnac topology 52% larger Event rate increased by a factor of 3.5

20. Comparison of results – BHBH optimisation - II S. ChelkowskiET Workshop, Erice, 10/2009Slide 20 RSE – optimised BHBH inspiral range for Sagnac topology 26% larger Event rate increased by a factor of 2.0 SAGNAC-optimised

21. Comparison of results – NSNS optimisation - I S. ChelkowskiET Workshop, Erice, 10/2009Slide 21 RSE – tuned SR BHBH inspiral range for Sagnac topology 47% larger Event rate increased by a factor of 3.2 SAGNAC-optimised

22. Comparison of results – NSNS optimisation - II S. ChelkowskiET Workshop, Erice, 10/2009Slide 22 RSE – optimised BHBH inspiral range for Sagnac topology 35% larger Event rate increased by a factor of 2.5 SAGNAC-optimised

23. Conclusion  Sagnac topology performs better than RSE in this analysis  BHBH inspiral range increase by 26%-52%  NSNS inspiral range increase by 35%-47%  Next steps:  Use Markov Chain Monte Carlo method for the optimisation, first tests already done  How does it perform?  Useful for comparison of other topologies in the future  Write ET note about this topology analysis  Think about alignment requirement changes for 4-mirror cavity S. ChelkowskiET Workshop, Erice, 10/2009Slide 23

24. S. ChelkowskiET Workshop, Erice, 10/2009Slide 24 THE END...

25. Additional slides S. ChelkowskiET Workshop, Erice, 10/2009Slide 25

26. PRM transmissivity algorithm check - I S. ChelkowskiET Workshop, Erice, 10/2009Slide 26 Tuning T prm for a fixed config

27. PRM transmissivity algorithm check - I S. ChelkowskiET Workshop, Erice, 10/2009Slide 27 Auto optimisationManual optimisation