max planck institute for gravitational physics and a week ... · m hewitson, amaldi 11, gwangju,...

Albert Einstein Institute

Max Planck Institute for Gravitational Physics and Leibniz Universität Hannover

Martin Hewitson for the LPF Team

Amaldi 11 Gwangju, South Korea

June 25th 2015

A week in the life of LISA Pathfinder Data Analysis

M Hewitson, Amaldi 11, Gwangju, June 25th 2015

Flow of talk

● Science signals and goals ● Analysis activities ● Building a noise budget ● Analysis of Week 1 simulation

2


LP Data Analysis Activities

● Analysis software and infrastructure

● Defining, simulating and testing experiments

● Analysis pipelines

● End-to-end tests (including hardware wherever possible)

● Interfacing with ESA’s STOC and MOC3


Analysis software and infrastructure

● LTPDA Toolbox ● MATLAB toolbox which implements an object-oriented data

analysis environment ● objects track their history so results are traceable and

reproducible ● heavily tested and documented

● ~700 page user manual ● ~6000 unit tests running every 3 hours ● multiple system test campaigns ● formal deliveries to ESA with acceptance tests

● LTPDA Repository ● provides a centralised database structure with web interface

for administration and searching ● interface to LTPDA toolbox directly from within MATLAB to

submit and retrieve objects ● core client/server system to be used by ESA for LPF mission ● also in heavy daily use in various labs

4

END

setName('NAME'='Residual')

plus(empty-plist)

plus(empty-plist)

minus(empty-plist)

split('OFFSETS'=[100 -100])


setName('NAME'='diff acc')

diff('METHOD'='3POINT', 'F0'='1/Nsecs', 'ORDER'='ZERO',

'COEFF'=[])

diff('METHOD'='3POINT', 'F0'='1/Nsecs', 'ORDER'='ZERO',

'COEFF'=[])

fftfilt('NPAD'=[], 'FILTER'='', 'INITIAL CONDITIONS'={}

[0x0])


split('START_TIME'='2012-11-2608:00:00', 'END_TIME'='2012-11-26

16:00:00')

index('I'=1, 'J'=[])

ao('BUILT-IN'='retrieve_in_timespan','START TIME'=2012-11-26 08:00:00.000

UTC, 'NSECS'=86400, 'NAMES'='LAT10005', 'HOSTNAME'='lpsdas01.esac.esa.int',

'DATABASE'='STOC_Sim_2_20121126_Full','VERSION'='Version 1', 'CONN'=[], 'BINARY'=1, 'STOP TIME'=

'1970-01-01 00:00:00.000','TIME RANGE'=[], 'CONSTRAINTS'='', 'FSTOL'=9.9999999999999995e-07,

'SORT'=1)

pzmodel('NAME'='', 'DESCRIPTION'='', 'GAIN'=1, 'POLES'=[],

'ZEROS'=[], 'IUNITS'='', 'OUNITS'='', 'DELAY'=-0.40000000000000002)


[0x0])


setName('NAME'='S_cmd')

mtimes(empty-plist)

ao('VALS'=2.1000000000000001,'DY'=[], 'AXIS'='y', 'N'=1,

'YUNITS'='', 'NAME'='', 'DESCRIPTION'='')


[0x0])


rdivide(empty-plist)


16:00:00')

index('I'=1, 'J'=[])


UTC, 'NSECS'=86400, 'NAMES'='ADT20112', 'HOSTNAME'='lpsdas01.esac.esa.int',



'SORT'=1)

ao('BUILT-IN'='tm_parameters','PARAMETER NAME'='EOM_TM2_M',

'VERSION'='Initial version')

pzmodel('NAME'='', 'DESCRIPTION'='', 'GAIN'=1, 'POLES'=[],

'ZEROS'=[], 'IUNITS'='', 'OUNITS'='', 'DELAY'=0.10000000000000001)


setName('NAME'='S_w2')

mtimes(empty-plist)

ao('VALS'=-2.1349999999999999e-06,'DY'=[], 'AXIS'='y', 'N'=1,

'YUNITS'='s^-2', 'NAME'='','DESCRIPTION'='')


setName('NAME'='S_dw')

mtimes(empty-plist)

ao('VALS'=-7.4700000000000001e-07,'DY'=[], 'AXIS'='y', 'N'=1,

'YUNITS'='s^-2', 'NAME'='','DESCRIPTION'='')



16:00:00')

index('I'=1, 'J'=[])


UTC, 'NSECS'=86400, 'NAMES'='LAT10003', 'HOSTNAME'='lpsdas01.esac.esa.int',



'SORT'=1)

http://www.lisa.uni-hannover.de/ltpda/

http://www.lisa.uni-hannover.de/ltpda/

M Hewitson, GWPAW, Osaka, June 18th 2015

LTPDA Usage for LPF

5

MATLAB

utility functions

utility classes

user classes

Pipelines

LTPDA Core Toolbox

LPF LTPDA Extension ModuleSystem Models Driver Scripts


Science goals

● Obtain the best geodesic motion possible ● quietest differential acceleration of the two TMs ● 3 x 10-14 m s-2 /√Hz at 1 mHz

● ~few pm accuracy position measurement of TM-SC, TM-TM ● optimisation by changing system parameters ● determine best configuration by experiments

● Develop the physical noise model of the system ● fully explain the observations we make ● allows the projection of the performance of technologies

to LISA

6


From Observables to Science Signal

● We observe relative motions of the two TMs and SC ● From these we estimate the residual differential force

per unit mass acting on the two TMs ● this requires some knowledge of the system ● applied control forces ● cross-talk terms

● To extrapolate to eLISA performance we subtract force terms which won’t be present ● primarily force gradient terms from electrostatic actuation

along x

7


Estimating Residual Differential Acceleration: Δg

● Understanding the purity of the free-fall we achieve, and what limits it, requires us to assess the residual forces acting on the TMs ● what’s left when we subtract the forces we can account for

● We compute the relative acceleration of the two TMs based on the observed relative position

● Try to account for the contributions of Δg that we know ● applied control forces ● couplings due to force gradients

8

�g =d2x

12

dt2�g

control

�!

2

�

x

1

� !

2

2

x

12


Noise budget: current best estimate

9


System Identification

Goal is to measure the key parameters needed for estimating the residual differential acceleration

10

Guidance Input

Force on SCDiff. X

SC X


Primary analysis approaches

● Spectral estimation ● primary science goal, assessment of

residual differential acceleration ● coherence between signals ● formation of noise breakdown

● Fitting ● system identification investigations ● signal injections ● dynamical, thermal, magnetic, etc…

11

10−4 10−3 10−2 10−110−15

10−14

10−13

10−12

10−11

10−10

10−9

[m

s−2

Hz−

1/2 ]

Frequency [Hz]

sqrt(PSD(In−loop Acceleration))sqrt(PSD(Residuals))sqrt(PSD(SC Jitter))sqrt(PSD(New Residuals))

SC Dynamics[m/N]

TM2 Dynamics[m/N]

Suspension Controller

Ftm2

x12dT

X12 IFO

x1

Fsc

TM1 Dynamics[m/N]

dTX1 IFO

Drag-free Controller

x1

K1

K2

kg s-2

kg s-2

Asus

Athruster

guidanceinjection

guidanceinjection

400 500 600 700 800 900 1000−1.5

−1

−0.5

0

0.5

1

1.5 x 10−6

Am

plitu

de [

m]

Origin: 2012−11−26 08:00:01.000 − Time [m]

X1 IFOX12 IFO


Science Operations

● We have a large menu of possible investigations we can run

● How do we choose? ● keeping in mind the system could fail at any moment

● General philosophy: ● low-risk, gentle probing of the system first to gain

experience and to understand the state of the system ● move on to more invasive investigations and begin tuning

the system ● higher risk investigations are planned to be later in the

operations

12


Plan for Week 1

13

Performance measurement

Day 1

Charge estimatex-axis sys id

Day 2

Charge estimatePerformance

Day 3

Cross-talk η/z plane

Day 4

Charge estimateCross-talk φ/y plane

Day 5

Charge estimatePerformance

Day 6

Station keeping

Day 7

Establish baseline

noise levels

Primary calibration parameters

System stationarity

Check for expected system diagonally

System stationarity


In more detail…

14

LPF STOC Operations

Week1The following assumes that the charge on the masses is not high. In other words, either a discharge has been performed, or the charge rate is low. Obviously we also assume that we are ableto operate stably in Science Mode 1.2.

We also assume that the noise levels in the system are close to what we expect from the mission requirements. If the noise is significantly higher, then the strategy would need to be adapted.As a minimum signal levels would need to be reviewed, but possibly the entire approach would change to focus on identifying the excess noise.

DOY344: 10/12/15, (08:00 – 08:00)

Measure noise. Primary science measurement. Set baseline for science conditions.

08:00 – 08:15 Load SDM19 con_00019_load__:V1 [15']08:15 – 09:15 Transition to Sci1.2 con_sci22_sc12:V1 [60']09:15 – 08:00 Acceleration Noise Measurement inv00001 [1365']

DOY345: 11/12/15, (08:00 – 08:00)

Charge estimation so we start to build up a charge rate. Use lower levels of excitation as a first try.

If there was a charge estimate already in commissioning, then here we can already decide if a fast discharge is necessary at the end of the week (station keeping).

Low-level calibration of the primary actuators along x and cross-calibration of two sensors.Again, measure noise so we start to build up a picture of the stationarity of the system.

08:00 – 09:00 Charge Estimate TM1 (low amps) inv04011_003 [60']09:00 – 10:00 Charge Estimate TM2 (low amps) inv04021_003 [60']10:00 – 14:00 Drag-free Injections (100nm scale) inv01101_002 [240']14:00 – 18:00 Suspension Injections (100nm scale) inv01102_002 [240']18:00 – 08:00 Acceleration Noise Measurement inv00001 [840']

DOY346: 12/12/15, (08:00 – 08:00)

Measure charge, again to build up charge rate estimate to allow us to plan the discharging scheme. Here we use small signal levels again so as not to disturb the very long noise run that’s inprogress. Maybe we get that from commissioning, maybe not.

Long noise estimate. Again, looking for stationarity. A return to conditions of day0 after signal injections.

08:00 – 09:00 Charge Estimate TM1 (low amps) inv04011_003 [60']09:00 – 10:00 Charge Estimate TM2 (low amps) inv04021_003 [60']10:00 – 08:00 Acceleration Noise Measurement inv00001 [1320']

DOY347: 13/12/15, (08:00 – 08:00)

Low-amplitude cross-talk assessment. Check that we have the levels of cross-talk we expect and that there is no dominating offset in the system. Also allows the teams to start analysing suchdata sets early on.

Although this is seen as a large amount of data to analyse, we believe that the probing of the different degrees of freedom should be done as close together in time as possible, so that they areprobing the same system. We need clear and careful procedures here: what operational questions do we want to answer here, and what do we do with the answers? Primarily this is a set ofdata in the bank that can be analysed offline.

08:00 – 11:00 Acceleration Noise Measurement inv00001 [180']11:00 – 15:00 Guidance eta1 (low amps) inv01167_002 [240']15:00 – 19:00 Guidance eta2 (low amps) inv01173_002 [240']19:00 – 23:00 Guidance z1 inv01165:V001 [240']23:00 – 03:00 Guidance z2 inv01171:V001 [240']03:00 – 08:00 Guidance Eta inv01161:V001 [300']

DOY348: 14/12/15, (08:00 – 08:00)

Charge estimate with larger/standard signal levels to try to estimate concurrent charge rate.

Continue with cross-talk assessment.

08:00 – 09:00 Charge Estimate TM1 inv04011_002 [60']09:00 – 10:00 Charge Estimate TM2 inv04021_002 [60']10:00 – 14:00 Guidance phi1 (low amps) inv01168_003 [240']14:00 – 18:00 Guidance phi2 (low amps) inv01174_003 [240']18:00 – 22:00 Guidance y1 inv01164:V002 [240']22:00 – 02:00 Guidance y2 inv01170:V002 [240']02:00 – 07:00 Guidance Phi inv01162:V001 [300']

DOY349: 15/12/15, (08:00 – 08:00)

Charge estimate with low level signals again to minimise disturbance on the system for the following noise run.Long noise measurement to assess the state before going in to our first station keeping.


DOY350: 16/12/15, (08:00 – 08:00)

We include fast discharge here, but the results from day 1 charge estimate (or in worst case day 2) may render this unnecessary.

Basically the philosophy here is to return to science mode 1.2 as soon as possible with our ‘standard’ SDM active, and begin to collect noise.

08:00 – 20:00 Station Keeping MOC [720']20:00 – 20:15 Load SDM19 con_00019_load__:V1 [15']20:15 – 20:25 Transition back to Acc3 con_dfxx_acc3__:V1 [10']20:25 – 21:25 Settling time for Acc3 N/A [60']21:25 – 02:25 Transition back to Sci 1.2 con_acc3_sci12__:V1 [300']02:25 – 03:25 Settling time for Sci 1.2 N/A [60']

Themes

Investigation

MOC

Configuration

Spacer

Comments

Minutes

(Last updated: 22 Jun 2015 11:03:57)

LPF STOC Operations

Week1The following assumes that the charge on the masses is not high. In other words, either a discharge has been performed, or the charge rate is low. Obviously we also assume that we are ableto operate stably in Science Mode 1.2.

We also assume that the noise levels in the system are close to what we expect from the mission requirements. If the noise is significantly higher, then the strategy would need to be adapted.As a minimum signal levels would need to be reviewed, but possibly the entire approach would change to focus on identifying the excess noise.

DOY344: 10/12/15, (08:00 – 08:00)

Measure noise. Primary science measurement. Set baseline for science conditions.

08:00 – 08:15 Load SDM19 con_00019_load__:V1 [15']08:15 – 09:15 Transition to Sci1.2 con_sci22_sc12:V1 [60']09:15 – 08:00 Acceleration Noise Measurement inv00001 [1365']

DOY345: 11/12/15, (08:00 – 08:00)

Charge estimation so we start to build up a charge rate. Use lower levels of excitation as a first try.

If there was a charge estimate already in commissioning, then here we can already decide if a fast discharge is necessary at the end of the week (station keeping).

Low-level calibration of the primary actuators along x and cross-calibration of two sensors.Again, measure noise so we start to build up a picture of the stationarity of the system.

08:00 – 09:00 Charge Estimate TM1 (low amps) inv04011_003 [60']09:00 – 10:00 Charge Estimate TM2 (low amps) inv04021_003 [60']10:00 – 14:00 Drag-free Injections (100nm scale) inv01101_002 [240']14:00 – 18:00 Suspension Injections (100nm scale) inv01102_002 [240']18:00 – 08:00 Acceleration Noise Measurement inv00001 [840']

DOY346: 12/12/15, (08:00 – 08:00)

Measure charge, again to build up charge rate estimate to allow us to plan the discharging scheme. Here we use small signal levels again so as not to disturb the very long noise run that’s inprogress. Maybe we get that from commissioning, maybe not.

Long noise estimate. Again, looking for stationarity. A return to conditions of day0 after signal injections.


DOY347: 13/12/15, (08:00 – 08:00)

Low-amplitude cross-talk assessment. Check that we have the levels of cross-talk we expect and that there is no dominating offset in the system. Also allows the teams to start analysing suchdata sets early on.

Although this is seen as a large amount of data to analyse, we believe that the probing of the different degrees of freedom should be done as close together in time as possible, so that they areprobing the same system. We need clear and careful procedures here: what operational questions do we want to answer here, and what do we do with the answers? Primarily this is a set ofdata in the bank that can be analysed offline.

08:00 – 11:00 Acceleration Noise Measurement inv00001 [180']11:00 – 15:00 Guidance eta1 (low amps) inv01167_002 [240']15:00 – 19:00 Guidance eta2 (low amps) inv01173_002 [240']19:00 – 23:00 Guidance z1 inv01165:V001 [240']23:00 – 03:00 Guidance z2 inv01171:V001 [240']03:00 – 08:00 Guidance Eta inv01161:V001 [300']

DOY348: 14/12/15, (08:00 – 08:00)

Charge estimate with larger/standard signal levels to try to estimate concurrent charge rate.

Continue with cross-talk assessment.

08:00 – 09:00 Charge Estimate TM1 inv04011_002 [60']09:00 – 10:00 Charge Estimate TM2 inv04021_002 [60']10:00 – 14:00 Guidance phi1 (low amps) inv01168_003 [240']14:00 – 18:00 Guidance phi2 (low amps) inv01174_003 [240']18:00 – 22:00 Guidance y1 inv01164:V002 [240']22:00 – 02:00 Guidance y2 inv01170:V002 [240']02:00 – 07:00 Guidance Phi inv01162:V001 [300']

DOY349: 15/12/15, (08:00 – 08:00)

Charge estimate with low level signals again to minimise disturbance on the system for the following noise run.Long noise measurement to assess the state before going in to our first station keeping.


DOY350: 16/12/15, (08:00 – 08:00)

We include fast discharge here, but the results from day 1 charge estimate (or in worst case day 2) may render this unnecessary.

Basically the philosophy here is to return to science mode 1.2 as soon as possible with our ‘standard’ SDM active, and begin to collect noise.

08:00 – 20:00 Station Keeping MOC [720']20:00 – 20:15 Load SDM19 con_00019_load__:V1 [15']20:15 – 20:25 Transition back to Acc3 con_dfxx_acc3__:V1 [10']20:25 – 21:25 Settling time for Acc3 N/A [60']21:25 – 02:25 Transition back to Sci 1.2 con_acc3_sci12__:V1 [300']02:25 – 03:25 Settling time for Sci 1.2 N/A [60']

Themes

Investigation

MOC

Configuration

Spacer

Comments

Minutes

(Last updated: 22 Jun 2015 11:03:57)


From the first week…

15

● Primary system calibration parameters ● needed to recover science signal,

● We recover a long performance measurement ● residual test mass acceleration down to sub-mHz

● Establish charge rate and plan discharge scheme ● Estimate the levels of cross-talk in the system

● needed to allow us to plan/target particular activities ● Target for the noise budget


Noise budget target

16

Δg


Update OMS sensing from measurement

● We can estimate the level of sensing noise in the different interferometer outputs from the high-frequency part of the spectrum ● use assumption on noise shape at lower frequencies ● may be measurable with caged TMs

17


Updated noise budget

18


Measure TM susceptibility to 50%

● Suppose we estimate the TM susceptibility through a magnetic coil excitation investigation ● (not currently planned for week 1, but serves as an

example here) ● Consider an estimate of ● Affects:

● coupling of fluctuating interplanetary magnetic fields ● coupling of fluctuating SC magnetic fields

● Recompute noise budget

19

�TMb = (4± 1)⇥ 10�5


Measure TM susceptibility to 50%

20

What’s missing?


Week 1: in-loop acceleration

21

Charge Estimatesx-axis sys id η/z plane sys idφ/y plane sys id


Analysis steps1. Fit x-axis guidance injections to recover a baseline Δg

• this allows estimation of the spectrum of Δg for all noise-only segments as well as the segment with the x-axis injections

• at this point we have 2 segments: up to the 1st charge estimation (81900s), and from after the first charge estimation to the start of the 2nd charge estimation (50400s)

2. Fit η1 and η2 guidance injections 3. Fit z1 and z2 guidance injections to a Δg which includes the η1 and η2

contributions 4. Fit ΗSC guidance injections to Δg

• we can now add another segment to the Δg list: starting from the end of the 2nd charge estimation and finishing after the HSC injections (165600s)

5. Fit φ1 and φ2 guidance injections 6. Fit y1 and y2 guidance injections to a Δg which includes the φ1 and φ2

contributions 7. Fit ΘSC to Δg

• now we can include the segment starting from after the 3rd charge estimation to the start of the 4th charge estimation (75600s)

22


Fitting models to data

● We use a number of techniques to estimate the contribution of our models to Δg

● Each model represents a parametric non-linear combination of other observables

● Example for φ1 injections:

● Typically we minimise the residuals in a likelihood way:

● Fit in the frequency domain

23

minimise⌃(�g �m(~✓))

m(~d, ~✓) = ✓1�1[k] + ✓2d2�1[k]

dt2


Parameter estimation methods

● Linear and non-linear least squares minimisation ● requires model for the noise ● either from previous data for weighting ● low-order noise model and fit coefficients

● Likelihoods: ● standard ‘gaussian’ likelihood ● marginalised logarithmic likelihood ● no noise model required

● Likelihood sampling via MCMC24


Example: z1 guidance injections

● Inject sequence of sine waves in z1 control loop

● Results in angular (η) SC motion ● which we observe as TM rotation

● Signals appear in Δg ● once we account for η motion

● Fit a model to estimate z1 contribution

● Subtract from Δg

25

�g +�g⌘1 +�g⌘2 = �gres

+ p1

z̈1

+ p2

z̈21

+ p3

gcontz1


Results

26


Parameter estimates

27

Investigation Parameter Units Value Error Rel Error % Method

x-axis sys ID

τloop s 1.83E+00 4.30E-03 0.23 χ2 minimiser

Gsus 1.00E+00 1.88E-04 0.02 MCMC

Cap Act Pole Hz 1.76E-01 1.86E-03 1.06 χ2 minimiser

ω22 s-2 -9.73E-07 1.00E-08 1.03 MCMC

ω122 s-2 -6.60E-07 6.85E-09 1.04 MCMC

η1 sys id

d2η1/dt2 m/rad 4.77E-05 5.60E-06 11.74 MCMC

d2η12/dt2 m/rad2 -7.78E-01 4.40E-01 56.53 MCMC

GNη1→Fx m -1.87E-05 1.10E-05 58.82 MCMC

η2 sys idd2η22/dt2 m/rad2 1.97E+00 5.00E-01 25.42 MCMC

GNη2→Fx m -9.69E-05 5.72E-05 59.03 MCMC

z1 sys idd2z1/dt2 m/rad 6.27E-05 6.83E-07 1.09 MCMC

d2z12/dt2 m/rad2 -4.90E+01 8.20E+00 16.73 MCMC

z2 sys id

d2z2/dt2 m/rad -6.45E-05 9.82E-07 1.52 MCMC

d2z22/dt2 m/rad2 -5.15E+01 9.13E+00 17.73 MCMC

GFz2→Fx m -2.10E-03 8.20E-04 39.05 MCMC

Η SC sys id Fz1 - Fz2 0.001068 5.72E-05 5.35 MCMC

φ1 sys id φ1 m s-2 rad-1 1.40E-04 8.98E-05 64.14 MCMC

φ2 sys id φ2 m s-2 rad-1 -6.79E-05 1.55E-05 22.83 MCMC

y1 sys id

d2y1/dt2 m/rad -7.41E-06 8.00E-07 10.80 MCMC

d2y12/dt2 m/rad2 1.57E+01 6.10E+00 38.85 MCMC

GFy1→Fx m 6.20E-03 4.20E-03 67.74 MCMC

y2 sys id d2y2/dt2 m/rad 7.96E-06 5.74E-07 7.21 MCMC


Δg estimate

28


Δg noise segments

29


Δg spectra

30


Δg contributions

● From each of the investigations in week 1 we get a contribution to Δg

● Use these contribution models to assess a noise-only run ● Select noise run segment ● Take estimated parameters and model of the contribution ● Compute each contribution

31


Δg noise segment and contributions

32


Add cross-terms to Noise Budget

33


Summary

34

● Data analysis is mature and ready to go for LPF ● LTPDA Toolbox is ready ● Key investigations are designed and tested ● Pipelines are being finalised ● Analysis procedures being prepared

● Procedures for establishing and tracking the noise budget are being developed ● starting from models/expectations ● replace with measurements as the mission proceeds

● Ready for science operations!

M Hewitson, Amaldi 11, Gwangju, June 25th 2015 35

History Graph for [fminsearch(LLH)/eda5052d-166c-45fd-ac0e-309433ffc4c5]Generated from terminal

Created 2015-06-21 00:54:34.458 UTC

BuildChi2Minimiser.run

BuildLogLikelihood.run

BuildModel.run

IterativeSimplex.run

LPFInv.getData

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Preprocess.run

ao.ao

ao.plus

ao.power

ao.rdivide

ao.split

collection.subsref

mfh.mfh

mfh.subsref

pest.mcmcPlot

repository.synchronizeOperationalDBs

telemetry.MUSTXMLToAO

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DIAGNOSTICS=0, DEBUG=0, PRINTDIAGNOSTICS=1, FPRINT=100,

PRIOR='', ANNEAL='simul',MODELFREQDEPENDENT=1, SNR0=

200, FREQUENCIES VECTOR=200,DIFFSTEP=[], NGRID=[], STEPRANGES=

[], LOGA=1, RAND_STREAM=1x1[struct], NAME='LLH', TIME

SERIES MFH=[], NOISE MODEL=[], TRANSFORM={} [0x0], K0=

5, K1=1, BIN GROUPS=[], NOISEPARAMETERS INDEX=[], P0 NOISE=

[], ALPHA=10000, NU='common',ERROR RATIO=0.5)


setDescription(DESCRIPTION='LAT00003, LAT00003, GST30101

(original units: [m]), GST3010...)

mfh(BUILT-IN='custom', VERSION='ao', NAME='fa12_ao', FUNC='dTauD2(o12, p(1)) - p(2).*pz(

A_cmd_x2./Mtm2, p(5)) + fW2(p( ..., CONSTANTS={'o1', 'o12',

'A_cmd_x2', 'Mtm2'}, CONSTANTOBJECTS={ao.hist , ao.hist, ao.hist , ao.hist }, DELAY

TYPE='fftfilt', TRANSFORM={'double'}, DESCRIPTION='',

PARAM UNITS={} [0x0])



setDescription(DESCRIPTION='ADT69499, ADT69499, GST10158

(original units: [N]), GST1015...)


mfh(BUILT-IN='custom', VERSION='core', NAME='deltaG_eta_z2',

FUNC='deltaG + UN(p(1), '').*dx2dt2(z2) + UN(p(2), 'm^-1').*dx2dt..., CONSTANTS={'deltaG',

'z2', 'z2_2', 'F_cmd_z2'},CONSTANT OBJECTS={ao.hist

, ao.hist , ao.hist , ao.hist}, DELAY TYPE='fftfilt', TRANSFORM=

{'double'}, NUMERIC=1, DESCRIPTION='', XUNITS='s', YUNITS='m

s^-2')

setDescription(DESCRIPTION='[DOY345_xaxis_sysid](2015-12-11

10:00:00.000 UTC -> 2015-12...)

getObjectAtIndex(I=1, J=[])

setName(NAME='ParamEstimate.IterativeSimplex(DOY345_xaxis_sysid)')

setYunits(YUNITS=[s][][s][s^(-2)][s^(-2)][Hz])

collection(NAME='', DESCRIPTION='', OBJS={pest.hist , collection.hist

}, NAMES={'fit', 'acc'})

setNames(NAMES={'p1', 'p2','p3', 'p4', 'p5'})

fminsearch(PARAM NAMES={}[0x0], P0=fminsearch(chi2Min)(

{'p(1)', 'p(2)', 'p(3)', 'p(4)', 'p(5)'}, [1.8558931486916861;1.00003490920009;-9.7092572844902089e-07;-6.5324642608294317e-07;0.17060603628190885]),

TOLX=0.001, DISPLAY='iter',MAXITER=[], OPTIONS=[], OUTPUTFILE=[], SHOW PLOT=0, MODEL=[], DSTEP=[], APPLY NEGATIVE=

0, YUNITS=[])

setSubfuncs(SUBFUNCS=@fa12Wh(p): split(filter(detrend(split(

fa12_ao(double(p)), accTrimPl),accDetrendPl), wf), whiteAccTrimPl))

setSubfuncs(SUBFUNCS=@fa12Wh(p): split(filter(detrend(split(

fa12_ao(double(p)), accTrimPl),accDetrendPl), wf), whiteAccTrimPl))

setConstObjects(CONSTOBJECTS={26600, 6})

eval(INPUTOBJECTS={[1.8558931486916861;1.00003490920009;-9.7092572844902089e-07...]})

collection(NAME='', DESCRIPTION='', OBJS={ao.hist }, NAMES=

{'obj1'})





setName(NAME='o1_dmu')

setDescription(DESCRIPTION='LAT00003, GST30101 (originalunits: [m]), GST30101 (origina

...)

setYunits(YUNITS=[m])


split(TIMESPAN=(start: 2015-12-1110:00:00.000 UTC end: 2015-12-11

18:00:00.000 UTC))


...)


03:00:00.000 UTC))


join(ZEROFILL=0, SAMET0=0,FSTOL=9.9999999999999995e-07,

SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)

setDescription(DESCRIPTION='GST30101 (original units:

[m])')


timespan(NAME='', DESCRIPTION='', START=2015-12-10 08:00:00.000

UTC, STOP=2015-12-17 08:00:00.000UTC, TIMEZONE='UTC', TIMEFORMAT=

'')

ao(HOSTNAME='lpsdas03.esac.esa.int',DATABASE='20151210_F_sim_POR_firstWeekOps',

ID=187, CID=[], UUID={} [0x0],BINARY=1, NAME='', DESCRIPTION=

'', TIMESPAN=timespan.hist)

































































































































































[m])')



[m])')



[m])')



[m])')



[m])')








...)




18:00:00.000 UTC))


...)


03:00:00.000 UTC))



SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[m])')



[m])')



[m])')



[m])')



[m])')

setYunits(YUNITS=[m])setDescription(DESCRIPTION=

'GST30103 (original units:[m])')






setName(NAME='F_tot_x2')

setDescription(DESCRIPTION='ADT69499, GST10158 (originalunits: [N]), GST10158 (origina

...)

setYunits(YUNITS=[N])



18:00:00.000 UTC))


...)


03:00:00.000 UTC))



SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[N])')



[N])')



[N])')



[N])')



[N])')



[N])')








...)









...)









...)




(original units: [rad]), GST10...)



setName(NAME='DFACS_eta1_ifo')

setDescription(DESCRIPTION='ADT20034, GST10017 (original

units: [rad]), GST10017 (origi...)

setYunits(YUNITS=[rad])



03:00:00.000 UTC))





SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[rad])')



[rad])')



[rad])')



[rad])')



[rad])')



[rad])')



(original units: [N m]), GST10...)



setName(NAME='N_tot_eta1')

setDescription(DESCRIPTION='ADT69497, GST10156 (originalunits: [N m]), GST10156 (origi

...)

setYunits(YUNITS=[N m])



03:00:00.000 UTC))


...)



SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[N m])')



[N m])')



[N m])')



[N m])')



[N m])')



[N m])')












03:00:00.000 UTC))





SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[rad])')



[rad])')



[rad])')



[rad])')



[rad])')



[rad])')








...)




03:00:00.000 UTC))


...)



SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[N m])')



[N m])')



[N m])')



[N m])')



[N m])')



[N m])')






setName(NAME='DFACS_z2')

setDescription(DESCRIPTION='ADT20029, GST10012 (originalunits: [m]), GST10012 (origina

...)




03:00:00.000 UTC))

setDescription(DESCRIPTION='ADT20029, GST10012 (originalunits: [m]), GST10012 (origina

...)



SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[m])')



[m])')



[m])')



[m])')



[m])')



[m])')






setName(NAME='F_tot_z2')


...)




03:00:00.000 UTC))


...)



SORT=1)


SORT=1)


SORT=1)


SORT=1)


SORT=1)


[N])')



[N])')



[N])')



[N])')



[N])')



[N])')


fminsearch(PARAM NAMES={'z2_dd','z2^2_dd', 'Gact'}, P0=[1.0000000000000001e-05

0.10000000000000001 1], TOLX=[], DISPLAY='iter', MAXITER=

[], OPTIONS=[], OUTPUT FILE=[], SHOW PLOT=0, MODEL=[],

DSTEP=[], APPLY NEGATIVE=1,YUNITS=[])

setYunits(YUNITS=[m rad^(-1)][mrad^(-1) rad^(-1)][m])

computePdf(BURNIN=100, NBINS=60)

MCMC.mhsample(INNAMES='',OUTNAMES='', MODEL='', DIFF

MODEL='', FITPARAMS={'eta1_dd','eta1^2_dd', 'Gact'}, INPUT=

'', OUTPUT='', NSAMPLES=200000,COV=Covariance Matrix/cdataNdata=[3x3], yunits=, SCALE

COV=1, NOISE='', LOGLIKELIHOOD=@LLH(p): loglikelihood_core_log(

ts, p, Ns, olap, freqs, trim,win, k0, k1, fs, dt), RANGE=

{[-2 2], [-10 10], [-2 2]},SEARCH=1, PROPOSAL SAMPLER=

[], PROPOSAL PDF=[], HEAT=3, TC=[0 1], X0=fminsearch(LLH)({'eta1_dd', 'eta1^2_dd',

'Gact'}, [1.2070469292080634e-05;0.1288857645195873;5.3689953273904266e-05]),UPDATE FIM FREQ=[], JUMPS=

[2 100 1000 10000], PLOT TRACES=1, PLOT DIAGNOSTICS=0, DEBUG=0, PRINT DIAGNOSTICS=1, FPRINT=

5000, TXT=0, PRIOR='', LOGPARAMETERS='', ANNEAL='simul',

MODELFREQDEPENDENT=1, SNR0=200, FREQUENCIES VECTOR=200,

DIFFSTEP=[], NGRID=[], STEPRANGES=[], LOGA=1, RAND_STREAM=1x1

[struct])

setYunits(YUNITS=[m rad^(-1)][mrad^(-1) rad^(-1)][m])

computePdf(BURNIN=100, NBINS=60)

MCMC.mhsample(INNAMES='',OUTNAMES='', MODEL='', DIFF

MODEL='', FITPARAMS={'eta2_dd','eta2^2_dd', 'Gact'}, INPUT=

'', OUTPUT='', NSAMPLES=200000,COV=Covariance Matrix/cdataNdata=[3x3], yunits=, SCALE

COV=1, NOISE='', LOGLIKELIHOOD=@LLH(p): loglikelihood_core_log(

ts, p, Ns, olap, freqs, trim,win, k0, k1, fs, dt), RANGE=

{[-2 2], [-10 10], [-2 2]},SEARCH=1, PROPOSAL SAMPLER=

[], PROPOSAL PDF=[], HEAT=3, TC=[0 1], X0=fminsearch(LLH)({'eta2_dd', 'eta2^2_dd',

'Gact'}, [1.207267924801375e-05;0.12889726228966575;-9.9633166737639795e-05]),UPDATE FIM FREQ=[], JUMPS=

[2 100 1000 10000], PLOT TRACES=1, PLOT DIAGNOSTICS=0, DEBUG=0, PRINT DIAGNOSTICS=1, FPRINT=

5000, TXT=0, PRIOR='', LOGPARAMETERS='', ANNEAL='simul',

MODELFREQDEPENDENT=1, SNR0=200, FREQUENCIES VECTOR=200,

DIFFSTEP=[], NGRID=[], STEPRANGES=[], LOGA=1, RAND_STREAM=1x1

[struct])



18:00:01.020 UTC))

setReferenceTime(T0=2015-12-1110:00:00.000 UTC)

interp(VERTICES='93589.399999755856697:1.000000000000000:122388.399999755856..., METHOD='auto')

resample(FSOUT=1, FILTER=[])

fixfs(FS=10, METHOD='time',FILTER='off', INTERPOLATION=

'auto')

dropduplicates(TOL=0.0050000000000000001)



18:00:01.020 UTC))





'auto')




18:00:01.020 UTC))





'auto')



mfh(BUILT-IN='custom', NAME='DiffAcc', FUNC='dTauD2(o12,

p(1)) - p(2).*pz(A_cmd_x2,p(5)) + fW2(p(3)).*d ...,

NUMERIC=0, CONSTANTS={'o1','o12', 'A_cmd_x2'}, TRANSFORM='double', CONSTANT OBJECTS=

{ao.hist , ao.hist , ao.hist}, DESCRIPTION='', VERSION=

[], DELAY TYPE='fractional',XUNITS='s', YUNITS='m s^-2')


02:59:59.223 UTC))





'auto')




02:59:59.223 UTC))





'auto')





02:59:59.223 UTC))





'auto')




02:59:59.223 UTC))


02:59:59.223 UTC))





'auto')




02:59:59.223 UTC))


02:59:59.223 UTC))





'auto')




02:59:59.223 UTC))


02:59:59.223 UTC))





'auto')




02:59:59.223 UTC))


02:59:59.223 UTC))





'auto')



power(empty-plist)


02:59:59.223 UTC))





'auto')





02:59:59.223 UTC))





'auto')


ao(BUILT-IN='tm_parameters',PARAMETER NAME='EOM_TM2_M',

VERSION=[], NAME='', DESCRIPTION='', TIMESPAN=[])



ao(BUILT-IN='tm_parameters',PARAMETER NAME='EOM_TM1_IYY',VERSION=[], NAME='', DESCRIPTION=

'', TIMESPAN=[])


ao(BUILT-IN='tm_parameters',PARAMETER NAME='EOM_TM2_IYY',VERSION=[], NAME='', DESCRIPTION=

'', TIMESPAN=[])




plus(empty-plist)

plus(empty-plist)

power(empty-plist)

mfh(BUILT-IN='custom', NAME='DeltaG_eta1', FUNC='UN(p(

1), 'm/rad').*dx2dt2(eta1)+ UN(p(2), 'm/rad/rad').*dx

..., NUMERIC=0, CONSTANTS={'eta1', 'eta1_2', 'Neta1'},

TRANSFORM='double', CONSTANTOBJECTS={ao.hist , ao.hist

, ao.hist }, DESCRIPTION='',VERSION=[], DELAY TYPE='fractional',

XUNITS='s', YUNITS='m s^-2')

ao(VALS=2, DY=[], AXIS='y',N=1, YUNITS=[], NAME='', DESCRIPTION=

'', TIMESPAN=[])

power(empty-plist)

mfh(BUILT-IN='custom', NAME='DeltaG_eta2', FUNC='UN(p(

1), 'm/rad').*dx2dt2(eta2)+ UN(p(2), 'm/rad/rad').*dx

..., NUMERIC=0, CONSTANTS={'eta2', 'eta2_2', 'Neta2'},

TRANSFORM='double', CONSTANTOBJECTS={ao.hist , ao.hist

, ao.hist }, DESCRIPTION='',VERSION=[], DELAY TYPE='fractional',

XUNITS='s', YUNITS='m s^-2')


'', TIMESPAN=[])


'', TIMESPAN=[])

eval(INPUTOBJECTS={pest.hist})



ao(HOSTNAME='195.74.167.200',DATABASE='20151210_F_sim_POR_firstWeekOps',

ID=187, CID=[], BINARY=1,NAME='', DESCRIPTION='', TIMESPAN=

[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])


ID=54, CID=[], BINARY=1, NAME='', DESCRIPTION='', TIMESPAN=

[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



[])



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[])



[])

setProcinfo(PROCINFO=(AOCONVERTER='May 2015', AOCONFIG='', MUSTFILE=

'LISA Pathfinder Telemetryfrom XML AO STOC/LPF00_F_15344080

..., SUBSYSTEM='LPF', PARAMETERID='GST30101', TELEMETRY TYPE=

'F', PACKET REFERENCE='00',PACKET ID='________', ON-BOARDDATE='2015/344', POR VERSION=

'99'))

setDescription(DESCRIPTION='GST30101')





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Thanks!

max planck institute for gravitational physics and a week ... · m hewitson, amaldi 11, gwangju,...

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