1. Barcelona 26-vi-2007DDS Data Analysis1 DDS Data Analysis, II Alberto Lobo ICE-CSIC & IEEC

2. Barcelona 26-vi-2007DDS Data Analysis2 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

3. Barcelona 26-vi-2007DDS Data Analysis3 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

4. Barcelona 26-vi-2007DDS Data Analysis4 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.LTP-wide reference model 4.Data Analysis Procedures

5. Barcelona 26-vi-2007DDS Data Analysis5 Thermal 22 NTC temperature sensors 16 heaters

6. Barcelona 26-vi-2007DDS Data Analysis6 Thermal

7. Barcelona 26-vi-2007DDS Data Analysis7 Thermal Optical Window

8. Barcelona 26-vi-2007DDS Data Analysis8 Thermal Optical Window Heaters Heater

9. Barcelona 26-vi-2007DDS Data Analysis9 Thermal Optical Bench Temperature Sensors

10. Barcelona 26-vi-2007DDS Data Analysis10 Thermal Suspension Struts: Heaters and Sensors

11. Barcelona 26-vi-2007DDS Data Analysis11 The Problem COMPLETED WORK: Progress towards: - DDS Heaters sizing (P, SNR, dT …) - OW heaters comparison with experiment - Modelling, analytic or SW tool - Others – Quantify thermal gradients between any two points, given the DDS sensors measurements.

12. Barcelona 26-vi-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. Barcelona 26-vi-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. Barcelona 26-vi-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. Barcelona 26-vi-2007DDS Data Analysis15 LCA Thermal Model, v8 S2-CGS-TN-3031

16. Barcelona 26-vi-2007DDS Data Analysis16 2) Apply sinusoidal input, T J = 1 · sin (  0 t) Proposal: Frequency Sweep y(n) = h(z) x(n) y(n) = y std (n) + y trs (n) = = A exp (i  n+  ) H(  ) + y trs (n) x(n) = A exp (i  n ) n>0 WHAT: Evaluate transfer functions points at a fixed frequency HOW: Fitting exponential decay 1) Select input node: node = J 3) Fit results on different node (K) peak values: y K, max (n) = A exp (- n / T) + C 4) Get transfer function node K wrt node J G KJ (  0 ) = C

17. Barcelona 26-vi-2007DDS Data Analysis17 Hacking FITTING: Simulation results sampled at the input frequency from the maximum in the last period to some selected point after transient. Substract initial value afterwards. [a,b,c,da,db,dc] = F(y,Tin,node) 1 mHz 5 mHz 50 mHz

18. Barcelona 26-vi-2007DDS Data Analysis18 All heaters OFF Temperature measurements to be translated into LTP signals (TM accelerations and/or laser metrology phase shifts) by transfer function scaling. Has ambiguities unless a suitable model of the physical processes is available. 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

19. Barcelona 26-vi-2007DDS Data Analysis19 Application: OB Sensors Location Sensors Input Sensors

20. Barcelona 26-vi-2007DDS Data Analysis20 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

21. Barcelona 26-vi-2007DDS Data Analysis21 LCA

22. Barcelona 26-vi-2007DDS Data Analysis22 Magnetometer available areas

23. Barcelona 26-vi-2007DDS Data Analysis23 Magnetometers’ accommodation

24. Barcelona 26-vi-2007DDS Data Analysis24 Coil Accommodation

25. Barcelona 26-vi-2007DDS Data Analysis25 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

26. Barcelona 26-vi-2007DDS Data Analysis26 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

27. Barcelona 26-vi-2007DDS Data Analysis27 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

28. Barcelona 26-vi-2007DDS Data Analysis28 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.

29. Barcelona 26-vi-2007DDS Data Analysis29 LCA and Magnetic sources Magnetic sources: Around 50 identified Distributed outside LCA Their positions are known They behave mostly as magnetic dipoles Dipole moments are unknown Solar panel is no such

30. Barcelona 26-vi-2007DDS Data Analysis30 LCA and Magnetic sources

31. Barcelona 26-vi-2007DDS Data Analysis31 Magnetic field map

32. Barcelona 26-vi-2007DDS Data Analysis32 Magnetic field reconstruction Exact reconstruction not possible with 4 magnetometers and around 50 dipole sources (+solar panel) Tentative approaches attempted so far: Linear interpolation Weighted interpolation –various schemes Statistical simulation (“equivalent sources”) Latest idea: Multipole field structure estimation: Fully possible up to quadrupole approximation Partly up to octupole –and tricky, but not impossible...

33. Barcelona 26-vi-2007DDS Data Analysis33 Multipole reconstruction theory In vacuum, A multipole expansion of B follows that corresponding to  (x) : The coefficients a lm (r) depend on the magnetisation M(x). In an obvious notation, structure is:

34. Barcelona 26-vi-2007DDS Data Analysis34 Multipole reconstruction theory Evaluation of multipole terms is based on some assumptions: 1. Magnetisation is due to magnetic dipoles only 2. Such dipoles are outside the LCA. Somewhat legthy calculations lead to: with

35. Barcelona 26-vi-2007DDS Data Analysis35 Multipole reconstruction theory Idea is now to fit measured field values to a limited multipole expansion model. Arithmetic sets such limit to quadrupole: Fit criterion is to minimise squared error:

36. Barcelona 26-vi-2007DDS Data Analysis36 Arithmetics of reconstruction algorithm: Data channels: 12 M lm dipole: 3 M lm quadrupole: 5 M lm octupole: 7 Multipole reconstruction theory 3+5 = 8 some redundancy, OK these 3 are uniform field components 5+7 = 12 exactly!! Summing up: Full quadrupole structure up to quadrupole level –even redundant Fully possible up to octupole if constant dipole can be determined Errors in the order of 10% (TBC, could be better!)

37. Barcelona 26-vi-2007DDS Data Analysis37 Ideal error map L %TM1 %TM2 1 32.0 47.8 2 3.1 12.3 3 1.9 0.6

38. Barcelona 26-vi-2007DDS Data Analysis38 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.

39. Barcelona 26-vi-2007DDS Data Analysis39 Radiation Monitor

40. Barcelona 26-vi-2007DDS Data Analysis40 Radiation Monitor

41. Barcelona 26-vi-2007DDS Data Analysis41 Radiation Monitor

42. Barcelona 26-vi-2007DDS Data Analysis42 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.

43. Barcelona 26-vi-2007DDS Data Analysis43 Radiation Monitor Radiation Monitor data are formatted in a histogram-like form. A histogram is generated and sent (to OBC) every 614.4 sec.

44. Barcelona 26-vi-2007DDS Data Analysis44 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

45. Barcelona 26-vi-2007DDS Data Analysis45 End of Presentation

46. Barcelona 26-vi-2007DDS Data Analysis46 Radiation Monitor GCR SEP

47. Barcelona 26-vi-2007DDS Data Analysis47 Radiation Monitor

48. Barcelona 26-vi-2007DDS Data Analysis48 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