1. Online Veto Analysis of TAMA300 Daisuke Tatsumi National Astronomical Observatory of Japan The TAMA Collaboration 8 th GWDAW19 Dec 2003 @ Milwaukee, UWM, USA

2. Introduction To distinguish GW signals from noises, we should identify the noise sources. In TAMA case, several noise contributions were already evaluated in the frequency domain as shown in this figure.

3. Because detector conditions will be changed, we need to monitor all of noises continuously and in time. For example, a mean level of some noise do not contaminate the displacement noise. But non-stationary noises may influence. Even in such case, if we monitor the noise contamination continuously, we can distinguish the noise from GW signals. For the veto analysis, it is very important to evaluate noise contamination continuously. Because detector conditions will be changed, we need to monitor all of noises continuously and in time. For example, a mean level of some noise do not contaminate the displacement noise. But non-stationary noises may influence. Even in such case, if we monitor the noise contamination continuously, we can distinguish the noise from GW signals. For the veto analysis, it is very important to evaluate noise contamination continuously. Introduction

4. Contents We began to study Veto Analysis intended to We began to study Veto Analysis intended to the following noises: 1.Differential motion of Power Recycled Michelson (Hereafter it is called slm: small l minus) 2.Laser Intensity Noise (int) By focusing on these, I talk about current status of Checking of the noise contaminationmechanism Checking of the noise contamination mechanism Online evaluation of these noise contaminations Online evaluation of these noise contaminations

5. This is a schematic view of noise contamination mechanism on slm. Slm is controlled at low frequency region below 20 Hz. In other words, at the observation band, it is not controlled. So we can consider that the noise contaminate via this path with a coupling constant of epsilon. Noise Transfer Function = V4 / V2 To confirm this model, we measured noise transfer function from slm to the displacement noise. Noise Contamination Mechanism (slm noise) H slm D slm F slm A slm (slm) - WF slm H D F A (llm) - WF er V2V2 V4V4 UGF: 20Hz coupling constant

6. Noise Transfer Function (slm noise) Inconsistent with measurement. But the model is not consistent with measurement.

7. Laser l1l1 l2l2 slm = l 1 - l 2 Laser l1l1 l2l2 slm = l 1 - l 2 Compound mirror Simple Power-Recycled Michelson The origin of the difference This difference come from our incorrect assumption. We could not consider the slm to such a simple Power-Recycled Michelson. We should consider the slm to Power-Recycled Michelson with compound end mirrors. It means its reflectivity has frequency dependence.

8. Noise Contamination Mechanism (slm noise) H slm D slm F slm A slm (slm) - WF slm H D F A (llm) - WF er V2V2 V4V4 UGF: 20Hz H coupling constant We modified the model by taking into account such compound mirror effect as H.

9. Noise Transfer Function (slm noise) We confirmed that the modified model is consistent with measurement.

10. Noise Contamination Mechanism (Intensity Noise) H INT D INT F INT A INT (INT) - WF INT H D F A (llm) - WF er Intensity Noise D INT V4V4 V3V3 UGF: 50kHz coupling constant Next is intensity noise. It is also modeled in a similar way. But, because the intensity noise is controlled at observation band, only the suppressed intensity noise contaminate to the displacement noise with a coupling constant of epsilon. Noise Transfer Function = V4 / V3 To confirm this model, we measured transfer function.

11. Noise Transfer Function (Intensity Noise) Inconsistent with measurement. The amplitude is consistent, but the phase is not consistent.

12. Transfer Function (  T) The difference suggests us that this kind of all-path filter is necessary. But unfortunately we cannot understand why this filter is needed. Now numerical approach on this program is going on in our group.

13. Noise Contamination Mechanism (Intensity Noise) H INT D INT F INT A INT (INT) - WF INT H D F A (llm) - WF er TT Intensity Noise D INT V4V4 V3V3 UGF: 50kHz coupling constant Anyway we constructed model of noise contamination experimentally.

14. Noise Transfer Function (Intensity Noise) And we confirm the model is consistent with measurement.

15. Online evaluation of noise contamination Noise contamination mechanisms were modeled and were measured as transfer function. So we can evaluate noise contamination by using auxiliary noise spectrum. Moreover, in the online evaluation, coupling constants are also monitored by using calibration peaks to follow changing of the detector condition.

16. Calibration Peaks for Noise Calibration slm noise Intensity noise To monitor the coupling constant, sinusoidal wave signals were injected into each control system.

17. Noise Contamination (displacement L-, slm, Intensity) This figure shows displacement noise spectrum, black is total noise. And green and purple are slm and intensity noise contamination, respectively.

18. Noise Contamination (displacement L-, slm, Intensity) To enhance the Intensity Noise 1. Intensity Servo vary OFF Servo vary OFF 2. Add offset on l- Contamination of Intensity noise is well consistent with displacement noise

19. Summary To realize online veto analysis, 1. We check the noise contamination mechanisms of slm and intensity noises. 2. We demonstrate online evaluation of the noise contaminations. In progress, 1.Increasing the number of monitored noise: alignment, frequency noise and so on. 2. Noise reduction by using this system.

20. Checking Transfer Function H INT D INT F INT A INT - WF INT D INT V3V3 VsVs V 3 / V s