1. Hannover 27-iv-2007DDS Data Analysis1 Alberto Lobo ICE-CSIC & IEEC

2. Hannover 27-iv-2007DDS Data Analysis2 DDS: Data Management & Diagnostics Subsystem Diagnostics items: Purpose: – Noise split up – Noise sources for LISA: spot route to required sensitivity Sensors for: – Temperature – Magnetic fields – Charged particles Calibration: – Heaters – Induction coils DMU: Purpose: – LTP computer Hardware: – Power Distribution Unit (PDU) – Data Acquisition Unit (DAU) – Data Processing Unit (DPU) Software: – Boot SW – Application SW: Diagnostics Phase-meter Interfaces

3. Hannover 27-iv-2007DDS Data Analysis3 Noise reduction philosophy Problem: to assess the contribution of a given perturbation to the total noise force f int. Approach: 1) Apply controlled perturbation  to the system 2) Measure “feed-through” coefficient between force and perturbation: 3) Measure actual  with suitable sensors 4) Estimate contribution of  by linear interpolation: 5) Substract out from total detected noise: 6) Iterate process for all identified perturbations

4. Hannover 27-iv-2007DDS Data Analysis4 Various diagnostics items  Temperature and temperature gradients: – Sensors: thermometers at suitable locations – Control: heaters at suitable locations  Magnetic fields and magnetic field gradients: – Sensors: magnetometers at suitable locations – Control: induction coils at suitable locations  Charged particle showers (mostly protons): – Sensors: Radiation Monitor – Control: non-existent

5. Hannover 27-iv-2007DDS Data Analysis5 General scheme for DDS DA (S2-IEC-TN-3031) For each diagnostic: 1.Measurement runs i.Controlled disturbance ON (if applicable) ii.Controlled disturbance OFF 2.Available data (in each case) 3.Data Analysis Procedures

6. Hannover 27-iv-2007DDS Data Analysis6 Thermal 22 NTC temperature sensors 16 heaters

7. Hannover 27-iv-2007DDS Data Analysis7 Thermal

8. Hannover 27-iv-2007DDS Data Analysis8 Thermal Optical Window

9. Hannover 27-iv-2007DDS Data Analysis9 Thermal Optical Window Heaters Heater

10. Hannover 27-iv-2007DDS Data Analysis10 Thermal Optical Bench Temperature Sensors

11. Hannover 27-iv-2007DDS Data Analysis11 Thermal Suspension Struts: Heaters and Sensors

12. Hannover 27-iv-2007DDS Data Analysis12 EH heaters: activation scheme P t  Heater set 2 Heater set 1  = 1000 sec Heaters signal Sensors response (CGS SW tool) T1T1 T4T4 T3T3 T2T2 T1T1 T4T4 T3T3 T2T2 H2H2 H1H1 H2H2 H1H1

13. Hannover 27-iv-2007DDS Data Analysis13 Heaters ON: EH Measurements: Temperatures T 1, T 2, T 3, T 4 per IS Accelerations a 1, a 2 per IS Laser Metrology x 1,  Main thermal signal:  T  (T 1  T 3 )  (T 2  T 4 ) per IS Data Analysis: fit data to Transfer function temperature-acceleration ensues

14. Hannover 27-iv-2007DDS Data Analysis14 Heaters ON: OW Measurements: Temperatures T 5, T 6 in IS1, T 11, T 12 in IS2 Laser Metrology x 1 for IS1, x 2  x 1  for IS2 Thermal signals: temperature closest to activated heater Data Analysis: fit data to ARMA(2,1): Should be OK in MBW –even beyond!–, and for each OW Can easily be improved, if necessary, at lower frequencies

15. Hannover 27-iv-2007DDS Data Analysis15 Heaters ON: suspension struts Measurements: Temperatures T i, i = 1,...,6 –all struts Laser Metrology x 1 and x 2  x 1  for each case Thermal signals: temperature closest to activated heater Data Analysis: Transfer function is a 6x2 matrix Estimated by standard methods Cross correlations likely to show up (?) Current shortage of experimental data   no sound a priori model available

16. Hannover 27-iv-2007DDS Data Analysis16 All heaters OFF Temperature measurements to be translated into LTP signals (TM accelerations and/or laser metrology phase shifts) by transfer function scaling. Cross correlations between different channels: Some can be (safely) discarded, e.g. OW-EH, etc. Others cannot, e.g., among different struts Global LTP system identification Some sensor readings used as housekeeping data, e.g., OB and redundant OW sensors Improved experimental characterisation needed and underway

17. Hannover 27-iv-2007DDS Data Analysis17 Magnetic disturbances in the LTP Magnetic noise is due to various causes: Random fluctuations of magnetic field and its gradient DC values of magnetic field and its gradient Remnant magnetic moment of TM and its fluctuations Residual high frequency magnetic fields Test masses are a AuPt alloy 0.7 Au + 0.3 Pt of low susceptibility and low remnant magnetic moment: a = 46 mm m = 1,96 kg

18. Hannover 27-iv-2007DDS Data Analysis18 LCA

19. Hannover 27-iv-2007DDS Data Analysis19 Magnetometer available areas

20. Hannover 27-iv-2007DDS Data Analysis20 Magnetometers’ accommodation

21. Hannover 27-iv-2007DDS Data Analysis21 Coil Accommodation

22. Hannover 27-iv-2007DDS Data Analysis22 Magnetic diagnostics: coils ON Philosophy: apply controlled periodic magnetic fields: Force comes then a two frequencies: – B 0 is calculated rather than measured with magnetometers – B bg is LTP background magnetic field

23. Hannover 27-iv-2007DDS Data Analysis23 Magnetic diagnostics: coils ON Data: Laser Metrology x 1 and x 2  x 1  for each VE being affected a 1 (a 2 ) from IS1 (IS2) if possible Coil feed intensity and frequency Analysis: from above data we can obtain are measured with good SNR (~ 100 max) are measured with poorer SNR

24. Hannover 27-iv-2007DDS Data Analysis24 Magnetic diagnostics: coils ON From F x,2  we can estimate  to ~1% From F y,2  and F z,2  we get error correction and cross check F  can be useful to estimate remnant magnetisation M This is more complicated, though: F x,  has (max) SNR ~ 100, but F y,  and F z,  quite less Yet all three components are needed, as M is a vector In addition, M needs to be disentangled from B bg

25. Hannover 27-iv-2007DDS Data Analysis25 Continuous magnetic field monitor Data: 4 3-axis magnetometers at fixed positions in LCA 12 sampled magnetic field channels Magnetic field and gradient must be known at TM locations: i. Magnetometer data streams are fed to suitable extrapolation algorithms ii. These algorithms are (so far) computationally demanding iii. To be run offline iv. They produce a magnetic field + gradient map around TMs v. Magnetic map error estimates will be delivered by the algorithm, too Processed data directly yield magnetic transfer function. Extrapolation operation errors need tight control.

26. Hannover 27-iv-2007DDS Data Analysis26 We need the magnetic field on the TMs region. For this, measuremrents provided by 4 3-axis magnetometers are available. There are (at present) 37 sources of magnetic disurbance identified (ASU). Magentometer information is thus insufficient to reconstruct the magnetic map. Magnetic Problem  The nominal magnitudes (moduli) of the magnetic moments of the sources are reasonalbly well known moments but their directions are not.

27. Hannover 27-iv-2007DDS Data Analysis27

28. Hannover 27-iv-2007DDS Data Analysis28 Radiation Monitor From S2-IEC-TN-3031:...The radiation monitor is primarily designed to help understand and quantify these variable processes [modulations of CGR and fluxes of SEP] by monitoring the external particle fluxes and allowing these to be correlated with the test-mass charge measurements.

29. Hannover 27-iv-2007DDS Data Analysis29 Radiation Monitor

30. Hannover 27-iv-2007DDS Data Analysis30 Radiation Monitor

31. Hannover 27-iv-2007DDS Data Analysis31 Radiation Monitor

32. Hannover 27-iv-2007DDS Data Analysis32 Radiation Monitor 1.Establish the charging-rate in the TMs due to cosmic-ray interactions. Compare with Monte Carlo simulations. Requires a long run with no UV lamps operating. 2.Establish the cosmic-ray transfer function from the radiation monitor to the test-mass charge. 3.Establish or limit the level of power spectral density of cosmic-ray modulations caused by solar activity. Provided by continuous operation of RM and other monitors available. 4.Establish the solar-energetic particle (SEP) flux enhancement distributions (temporal and fluence) seen by the radiation monitor. 5.Establish the solar-energetic particle transfer function from the radiation monitor to the test-mass charge. Done by cross-correlation of TM charge control data with RM (and other monitors) SEP data. 6.Estimate the solar-energetic particle induced charging rate and compare with simulations. 7.Demonstrate the closed loop charge control process and estimate its gain factor.

33. Hannover 27-iv-2007DDS Data Analysis33 Radiation Monitor Radiation Monitor data are formatted in a histogram-like form. A histogram is generated and sent (to OBC) every 614.4 sec.

34. Hannover 27-iv-2007DDS Data Analysis34 Radiation Monitor Additional data required: 1.Test mass charges, Q 1 and Q 2 every 1000-10,000 seconds to an accuracy to 10 4 elementary charges with sign. 2.ULU time status including lamps on/off and commanded UV levels 3.Inertial sensor noise power spectra 4.RM calibration data – channel to energy conversion 5.RM calibration data – efficiency factors for each spectral channel 6.RM calibration data – spectral resolution as function of energy 7.Updated satellite geometry model 8.Solar activity indicators

35. Hannover 27-iv-2007DDS Data Analysis35 End of Presentation

36. Hannover 27-iv-2007DDS Data Analysis36 Radiation Monitor GCR SEP

37. Hannover 27-iv-2007DDS Data Analysis37 Radiation Monitor

38. Hannover 27-iv-2007DDS Data Analysis38 Data handling issues: Front detector hits sent as flags Coincident events sent as energy deposed Electronics is able to cope with up to 5000 c/s, so data compression will be eventually needed. Testing issues: Artificially generated pulses Muon test Proton source exposition: PSI, end of October Radiation Monitor