hannover 27-iv-2007dds data analysis1 alberto lobo ice-csic & ieec

Hannover 27-iv-2007 DDS Data Analysis 1

DDSDDS Data Analysis Data Analysis

Alberto Lobo

ICE-CSIC & IEEC


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


Noise reduction philosophy

Problem: to assess the contribution of a given perturbation to the total noise force fint.

Approach: 1) Apply controlled perturbation to the system

2) Measure “feed-through” coefficient between force and perturbation:

int( )f

F

3) Measure actual with suitable sensors

4) Estimate contribution of by linear interpolation:

int ( ) ( )f F

5) Substract out from total detected noise:

red int int ( )f f f

6) Iterate process for all identified perturbations


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


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


Thermal

22 NTC

temperature

sensors

16 heaters


Thermal


Thermal

Optical Window


Thermal

Optical Window

Heaters

Heater


Thermal

Optical Bench

Temperature

Sensors


Thermal

Suspension

Struts: Heaters

and Sensors


EH heaters: activation scheme

P

t

Heater set 2

Heater set 1

= 1000 secHeaters signal

Sensors response(CGS SW tool)

T1

T4T3

T2

T1

T4T3

T2

H2H1

H2H1


Heaters ON: EH

Measurements: Temperatures T1, T2, T3, T4 per IS Accelerations a1, a2 per IS Laser Metrology x1,

Main thermal signal: T (T1T3) (T2T4) per IS

Data Analysis: fit data to

2

radiometer

1

2

pl Ta

m T

23

rad. press.

16

3

la T T

mc

Transfer function temperature-acceleration ensues


Heaters ON: OW

Measurements: Temperatures T5, T6 in IS1, T11, T12 in IS2

Laser Metrology x1 for IS1, x2 x1 for IS2

Thermal signals: temperature closest to activated heater

Data Analysis: fit data to ARMA(2,1):

1

1 1

1ˆ ˆ ˆ( ) ( ) ( )1 1

zz T z T z

z z

• Should be OK in MBW –even beyond!–, and for each OW

• Can easily be improved, if necessary, at lower frequencies


Heaters ON: suspension struts

Measurements: Temperatures Ti, i = 1,...,6 –all struts

Laser Metrology x1 and x2 x1 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


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


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

510

and low remnant magnetic moment:

8 20 10 A mm

a = 46 mmm = 1,96 kg


LCA


Magnetometer available areas


Magnetometers’ accommodation


CoilAccommodation


Magnetic diagnostics: coils ON

Philosophy: apply controlled periodic magnetic fields:

bg p 0p apap ( , ) ( ); i tt e B B xB xB B

Force comes then a two frequencies:

0 app bg app app bg0

V

F m B B B B B

2 app app0

V

F B B

– B0 is calculated rather than measured with magnetometers

– Bbg is LTP background magnetic field



Data:

• Laser Metrology x1 and x2 x1 for each VE being affected

• a1 (a2) from IS1 (IS2) if possible

• Coil feed intensity and frequency

Analysis: from above data we can obtain

, , , ,2 ,2 ,2, , and , ,x y z x y zF F F F F F

, ,2x xF F and are measured with good SNR (~ 100 max)

, , ,2 ,2, , ,y z y zF F F F are measured with poorer SNR



From Fx,2 we can estimate to ~1%

From Fy,2 and Fz,2 we get error correction and cross check

F can be useful to estimate remnant magnetisation M

This is more complicated, though:

• Fx, has (max) SNR ~ 100, but Fy, and Fz, quite less

• Yet all three components are needed, as M is a vector

• In addition, M needs to be disentangled from Bbg


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 algorithmsii. These algorithms are (so far) computationally demandingiii. To be run offlineiv. They produce a magnetic field + gradient map around TMsv. Magnetic map error estimates will be delivered by the algorithm, too

• Processed data directly yield magnetic transfer function.• Extrapolation operation errors need tight control.


• 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.

Hannover 27-iv-2007 DDS Data Analysis 270 5 10 15 20 25 30 35 400

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Source number

Mag

netic

fiel

d [

T]

Bt mag D modul

0 5 10 15 20 25 30 35 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Source number

Mag

netic

fiel

d [

T]

Bt mag C modul

0 5 10 15 20 25 30 35 400

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Source number

Mag

netic

fiel

d [

T]

Bt mag B modul

0 5 10 15 20 25 30 35 400

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Source number

Mag

netic

fiel

d [

T]

Bt mag A modul

0 5 10 15 20 25 30 35 400

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Source number

Mag

netic

fiel

d [

T]

B TM1 modul


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.


Radiation Monitor


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.


Radiation Monitor

Radiation Monitor data are formatted in a histogram-like form.A histogram is generated and sent (to OBC) every 614.4 sec.


Radiation Monitor

Additional data required:

1. Test mass charges, Q1 and Q2 every 1000-10,000 seconds to an accuracy to 104 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


End of PresentationEnd of Presentation


Radiation Monitor

GCRSEP


Radiation Monitor


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

hannover 27-iv-2007dds data analysis1 alberto lobo ice-csic & ieec

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