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Data Analysis for LISA Pathfinder
M Hewitson for the LPF teamGSFC, October 11th 2011
+----------------------------------------------------+| || || **** || ** || ------------- || //// / \\\\ || /// / \\\ || | / | || ** | +----+ / +----+ | ** || ***| | |//-------| | |*** || ** | +----+ /+----+ | ** || | / | || \\\ / /// || \\\\ // //// || ------------- || ** || **** || || Welcome to the LTPDA Toolbox || || Version: 2.4.dev || Release: (R2011b Prerelease) || Date: 22-06-11 || |+----------------------------------------------------+
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
The LTP DA Team
2
Martin HewitsonIngo DiepholzHeather AudleyPhil PetersonNatalia KorsakovaFelipe Guzman
Michele Armano
Miquel Nofrarias Marc Diaz Aguiló
Ferran GilbertNikos Karnesis
Mauro HuellerGiuseppe Congedo
Eric PlagniolLuigi Ferraioli
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011 3
Activities
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011 3
Activities
•DA Software• Toolbox of algorithms to act on the data• Repository for storing data products• Client for submitting and retrieving to/from repository• Documentation and testing
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011 3
Activities
•DA Software• Toolbox of algorithms to act on the data• Repository for storing data products• Client for submitting and retrieving to/from repository• Documentation and testing
•DA Activities• Interpret technical descriptions of experiment• Simulate experiments• Design, build and test analyses and associated tools• Train scientists
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA
4Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA•Main requirement:
• provide a data analysis environment suitable for analyzing the experiment data in ‘real-time’
4Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA•Main requirement:
• provide a data analysis environment suitable for analyzing the experiment data in ‘real-time’
•Additional requirements:• toolkit of fairly standard instrument characterisation
algorithms• results should be reproducible and accountable• reduce testing by building on a commercial product• ease of use (usable by non-experts)• simple data access for multiple users
4Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA•Main requirement:
• provide a data analysis environment suitable for analyzing the experiment data in ‘real-time’
•Additional requirements:• toolkit of fairly standard instrument characterisation
algorithms• results should be reproducible and accountable• reduce testing by building on a commercial product• ease of use (usable by non-experts)• simple data access for multiple users
4Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System Architecture
5
MATLAB
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System Architecture
5
MATLAB
utility functionsutility classesuser classes
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System Architecture
5
MATLAB
utility functionsutility classesuser classes
scripting layer GUI
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Objects, objects everywhere...
•LTPDA offers an object-oriented data analysis framework• we encapsulate (describe) different data analysis
concepts with classes• users instantiate (build) these classes to get ltpda
objects• DA algorithms are methods (functions) of these
different classes• users act on the objects using the methods
6
aoaoao algorithm ao
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Example: Analysis Objects
•We want to encapsulate the concept of an analysis result• avoid storing images, text files, documents, etc
7Wednesday, October 19, 11
Analysis Object
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Example: Analysis Objects
•We want to encapsulate the concept of an analysis result• avoid storing images, text files, documents, etc
7Wednesday, October 19, 11
Analysis Object
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Example: Analysis Objects
•We want to encapsulate the concept of an analysis result• avoid storing images, text files, documents, etc
7
Numerical Data
AdditionalMeta-data
Provenance
History Tree
Wednesday, October 19, 11
Analysis Object
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Example: Analysis Objects
•We want to encapsulate the concept of an analysis result• avoid storing images, text files, documents, etc
7
Numerical Data
AdditionalMeta-data
Provenance
History Tree
- name- data vectors- creation date- units- sample rate
- name- UUID- Comments- Processing Info- Plotting Info
- creator- date- IP address- hostname- operating system- software versions
- algorithm name- algorithm version- parameter list- date- input histories
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
AO Algorithms
8Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
AO Algorithms
8
>> methods ao
Contents detrend hist_gauss mdc1_ifo2acc_inloop rotate std abs dft hypot mdc1_ifo2cont_utn round straightLineFit acos diag ifft mdc1_ifo2control sDomainFit string angle diff imag mdc1_x2acc save submit ao display index mean scale sum asin dopplercorr integrate median scatterData sumjoin atan downsample interp min search svd atan2 dropduplicates interpmissing minus select svd_fit bilinfit dsmean inv mode setDescription t0 bin_data dx iplot mpower setDx table bsubmit dy iplotyy mrdivide setDy tan buildWhitener1D eig isprop mtimes setFs tdfit cat eq isvalid ne setMdlfile tfe char eqmotion join noisegen1D setName timeaverage cohere evaluateModel lcohere noisegen2D setPlotinfo timedomainfit complex exp lcpsd norm setProcinfo times compute export le normdist setT0 timeshift confint fft len nsecs setUUID transpose conj fftfilt linSubtract offset setX type consolidate filtSubtract lincom optSubtraction setXY uminus conv filter linedetect phase setXunits unwrap convert filtfilt linfit plot setY update copy find lisovfit plus setYunits upsample corr firwhiten ln polyfit setZ validate cos fixfs log polynomfit sign var cov fngen log10 power simplifyYunits viewHistory cpsd fromProcinfo lpsd psd sin whiten1D crbound fs lscov psdconf sineParams whiten2D created gapfilling lt pwelch smallvector_lincom x creator gapfillingoptim ltfe quasiSweptSine smallvectorfit xcorr csvexport ge ltp_ifo2acc rdivide smoother xfit ctranspose get max real sort xunits curvefit getdof mcmc rebuild spectrogram y delay gnuplot md5 removeVal spikecleaning yunits delayEstimate gt mdc1_cont2act_utn report split zDomainFit demux heterodyne mdc1_ifo2acc_fd resample spsd zeropad det hist mdc1_ifo2acc_fd_utn rms sqrt
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Don’t touch me with that AO, I don’t know where it’s been!
9
ao
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Don’t touch me with that AO, I don’t know where it’s been!
9
ao
name
history
version
data
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Don’t touch me with that AO, I don’t know where it’s been!
9
ao
name
history
version
data
name
input histories
version
params
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Don’t touch me with that AO, I don’t know where it’s been!
9
ao
name
history
version
data
name
input histories
version
params
name
input histories
version
params
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Don’t touch me with that AO, I don’t know where it’s been!
9
ao
name
history
version
data
name
input histories
version
params
name
input histories
version
params
name
input histories
version
paramsWednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Don’t touch me with that AO, I don’t know where it’s been!
9
ao
name
history
version
data
name
input histories
version
params
name
input histories
version
params
name
input histories
version
paramsWednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Don’t touch me with that AO, I don’t know where it’s been!
9
ao
name
history
version
data
name
input histories
version
params
name
input histories
version
params
name
input histories
version
paramsWednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
I love my mouse!
10Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
MATLAB
utility functionsutility classesuser classes
scripting layer GUI
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
MATLAB
utility functionsutility classesuser classes
scripting layer GUI
mysql
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
MATLAB
utility functionsutility classesuser classes
scripting layer GUI
mysql
php interface
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
MATLAB
utility functionsutility classesuser classes
scripting layer GUI
mysql www browser
php interface
apache/httpd
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Where can I stick my AO?
11
MATLAB
LTPDA Toolbox
Server
LTPDA Repository
MATLAB
utility functionsutility classesuser classes
scripting layer GUI
mysql www browser
php interface
apache/httpd
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA Repository
12Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA Repository
12
Manage User Accounts
Manage Databases
Browse/Query Databases
Submit/Retrieve Objects
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Extension modules
•LTPDA allows for extension modules• create your own user-classes• extend existing classes with new methods• add models, functions, etc
•Examples• LTP_DA_Module
• methods and functions directly for LTP DA• models of the system
• STOC_DA_Module• methods and function for use in the STOC
• Others used by labs etc
13Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Documentation
•Documentation• user-manual alone ~700 pages• ~1500 documented m-files (methods/functions)
14
ao analysis object class constructor. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DESCRIPTION: ao analysis object class constructor. Create an analysis object. Possible constructors: a = ao() - creates an empty analysis object a = ao('a1.xml') - creates a new ao by loading a file a = ao('a1.mat') a = ao('a1.mat') - creates a new ao by loading the 2-column data .MAT file. a = ao('file.txt') - creates a new ao by loading the data. a = ao('file.dat') a = ao('file',pl) (Set: From ASCII File) a = ao(data) - creates an ao with a data object. a = ao(constant) - creates an ao from a constant a = ao(specwin) - creates an ao from a specwin object a = ao(pzm) - creates an ao from a pole/zero model object a = ao(pzm,nsecs,fs) a = ao(smodel) - creates an ao from a symbolic model object a = ao(pest) - creates an ao from a parameter estimates object a = ao(x,y) - creates an ao with xy data a = ao(y, fs) - creates an ao with time-series data a = ao(x,y,fs) - creates an ao with time-series data a = ao(x,y,pl) - creates an ao depending from the PLIST (Set: From XY Values). a = ao(plist) - creates an ao from a parameter list Examples Parameters Description VERSION: $Id: ao.m,v 1.348 2011/05/16 07:15:37 hewitson Exp $ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Testing
•Unit-level tests• we run ~6000 unit tests every 3 hours
• some of these tests are dynamic (variable)•System level tests
• continuous system level testing in STOC exercises and Labs
• Some releases are system tested by professionals (FBK)• ~300 hand-run system tests• 10’s of thousands of automated tests
•Acceptance testing• each delivery to ESA is formally acceptance tested
15Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
The size of it
•224396 lines of MATLAB source code• not including extension
modules•60198 lines of HTML
documentation•~ 40000 CVS commits
since the beginning•~15 man years of effort
16Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA
17
•First serious release in April 2008•Last release May 2011•Next release August 2011•We have made three formal deliveries to
ESA• version 1.0, 2.0 and 2.4
•We have run a few dedicated training sessions at different institutions
•LTPDA is heavily used in different labs
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LTPDA
17
•First serious release in April 2008•Last release May 2011•Next release August 2011•We have made three formal deliveries to
ESA• version 1.0, 2.0 and 2.4
•We have run a few dedicated training sessions at different institutions
•LTPDA is heavily used in different labs
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Infrastructure
18
DataConverter
LTPDARepository
LTPDAClient
LTPDAClient
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Data Analysis Activities
•Modelling of LPF•Simulating the system•Noise projections•Simulating and analysing experiments
19Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Modelling LPF: goals
•Template generator• fast, containing only parameters which we want to
estimate•Simulations
• detailed models, can be slower and have more parameters
• aim to accurately reproduce observed noise
20Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Models in LTPDA
•Models are built as factory constructors of the different user classes• building a model produces an object of a particular
class•The template for making built-in models allows
for:• versioning• built-in documentation• easier testing
•Example:
21
% statespace model of LTPltp = ssm(plist('built-in', 'LTP'));
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Modelling LPF
22Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Modelling LPF
•We model LPF parts using different schemes• matrix models, symbolic models, statespace models
22Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Modelling LPF
•We model LPF parts using different schemes• matrix models, symbolic models, statespace models
•For simple experiments and/or validation, we can use simple 1D models
22Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Modelling LPF
•We model LPF parts using different schemes• matrix models, symbolic models, statespace models
•For simple experiments and/or validation, we can use simple 1D models
•For more realistic experiments (or in the mission) we need a full 3D description of LTP• for that we use statespace models
22Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What can you do with an SSM?
23Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What can you do with an SSM?
•Measure bode plots
23Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What can you do with an SSM?
•Measure bode plots•Run time-domain simulations
23Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What can you do with an SSM?
•Measure bode plots•Run time-domain simulations•Both of these allow us to generate parameter
dependent templates in time and frequency domain• we can use these for parameter estimation
• fitting to measured data
23Wednesday, October 19, 11
x = Ax+Bu
y = Cx+Du
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
How SSM models work
24Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Parts of LTP
25
Controllers
SensingDynamics
Actuators
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full system (current)
26Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
More advanced model
•We want to improve the model by making it more modular• Include more realistic sub-
system models• Include more physical features
27Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Parts of LTP (1)
OBCm
easu
rem
ent p
roce
ssingST
GRS
IFO
sens
or m
appi
ngDFACS
cont
rolle
rs
deco
uplin
g m
atrix
FEEP
Salg
orith
mCa
p Ac
talg
orith
m
Data
pro
cess
ing
guidance inputs
actuation inputs[m
,rad
]
[N,N
m] ActuationCapac Act
FEEPs
FEE
Mea
sure
men
t Pr
oces
sing
Actu
ator
M
odel
Mea
s.
Proc
essin
g
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Parts of LTP (1)
OBCm
easu
rem
ent p
roce
ssingST
GRS
IFO
sens
or m
appi
ngDFACS
cont
rolle
rs
deco
uplin
g m
atrix
FEEP
Salg
orith
mCa
p Ac
talg
orith
m
Data
pro
cess
ing
guidance inputs
actuation inputs[m
,rad
]
[N,N
m] ActuationCapac Act
FEEPs
FEE
Mea
sure
men
t Pr
oces
sing
Actu
ator
M
odel
Mea
s.
Proc
essin
g
+n +n dT+n dT
+n dT
+n dT
+n
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Parts of LTP (1)
29
FEEP
Salg
orith
mCa
p Ac
talg
orith
m
Data
pro
cess
ing
ActuationCapac Act
FEEPs
FEE
Actu
atio
nM
odel
Mea
sure
men
t Pr
oces
sing
Actu
ator
M
odel
Com
pute
Forc
es/T
orqu
es
Dynamics
[N,N
m]
[N,N
m]
[m,r
ad]
Equa
tions
ofM
otio
n
Mea
s.
Proc
essin
g
+n dT
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Parts of LTP (1)
29
FEEP
Salg
orith
mCa
p Ac
talg
orith
m
Data
pro
cess
ing
ActuationCapac Act
FEEPs
FEE
Actu
atio
nM
odel
Mea
sure
men
t Pr
oces
sing
Actu
ator
M
odel
Com
pute
Forc
es/T
orqu
es
Dynamics
[N,N
m]
[N,N
m]
[m,r
ad]
Equa
tions
ofM
otio
n
Mea
s.
Proc
essin
g
+n dT
+n dT +n
+n
+n dT
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full System
30
OMS
GRS
ST
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full System
30
OMS
GRS
ST
LTPDFACS
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full System
30
OMS
GRS
ST
LTPDFACS
Cap. Act.
FEEPs
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full System
30
OMS
GRS
ST
LTPDFACS
Cap. Act.
FEEPs
Dynamics
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full System
30
OMS
GRS
ST
LTPDFACS
DRSDFACS
Cap. Act.
FEEPs
Dynamics
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full System
30
OMS
GRS
ST
LTPDFACS
DRSDFACS
Cap. Act.
FEEPs
Colloids
Dynamics
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Full System
30
OMS
GRS
ST
LTPDFACS
DRSDFACS
Cap. Act.
FEEPs
Colloids
Dynamics
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Simulating the system
•We have two simulators at our disposal• The linear SSM model• The industry-provided, non-linear simulator (OSE)
31Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What is the OSE?
32Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What is the OSE?
•The OSE is the DFACS simulator produced by Astrium to verify performance of DFACS
32Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What is the OSE?
•The OSE is the DFACS simulator produced by Astrium to verify performance of DFACS
• It is a full 3D, non-linear, fairly detailed simulator of LPF
32Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What is the OSE?
•The OSE is the DFACS simulator produced by Astrium to verify performance of DFACS
• It is a full 3D, non-linear, fairly detailed simulator of LPF
• It is used as one of the ‘engines’ in the STOC simulator (LSS)
32Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What is the OSE?
•The OSE is the DFACS simulator produced by Astrium to verify performance of DFACS
• It is a full 3D, non-linear, fairly detailed simulator of LPF
• It is used as one of the ‘engines’ in the STOC simulator (LSS)
• It produces the closest data we can get to actual LPF data
32Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
What is the OSE?
•The OSE is the DFACS simulator produced by Astrium to verify performance of DFACS
• It is a full 3D, non-linear, fairly detailed simulator of LPF
• It is used as one of the ‘engines’ in the STOC simulator (LSS)
• It produces the closest data we can get to actual LPF data
• It is not really designed for use by end-users• reasonably difficult to use/configure• difficult to understand how it works, or why it does
some things the way it does
32Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Noise projections
33Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Noise projections
•Aim to explain the noise of the STOC Simulator (OSE) using our statespace linear model of LPF• iterative process to bring our model as close as
possible to the OSE• this closely resembles what we will do during the mission
33Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Noise projections
•Aim to explain the noise of the STOC Simulator (OSE) using our statespace linear model of LPF• iterative process to bring our model as close as
possible to the OSE• this closely resembles what we will do during the mission
•Simulator configured with:• M3 all-optical• Star Tracker noise switched on/off• nominal cross-couplings• All other noises nominal• test-mass 1 total stiffness = -1e-6• test-mass 2 total stiffness = -2e-6
33Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM1
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM13. Run simulation (~10000s)
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM13. Run simulation (~10000s)4. Save time series
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM13. Run simulation (~10000s)4. Save time series5. Make spectral density estimates of two longitudinal IFO
outputs (o1,o12)
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM13. Run simulation (~10000s)4. Save time series5. Make spectral density estimates of two longitudinal IFO
outputs (o1,o12)6. Convert these to acceleration (a1, adelta) and make spectral
density estimates
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM13. Run simulation (~10000s)4. Save time series5. Make spectral density estimates of two longitudinal IFO
outputs (o1,o12)6. Convert these to acceleration (a1, adelta) and make spectral
density estimates7. Repeat for other noise sources
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM13. Run simulation (~10000s)4. Save time series5. Make spectral density estimates of two longitudinal IFO
outputs (o1,o12)6. Convert these to acceleration (a1, adelta) and make spectral
density estimates7. Repeat for other noise sources8. Make correlated sum of all time-series
8.1.also convert this to acceleration and make spectral density estimates
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Making the SSM prediction1. We use our linear statespace model of LPF
1.1. The version used is using the same DFACS controllers as the OSE 2. Inject a single noise source, e.g., capacitive actuation noise
for TM13. Run simulation (~10000s)4. Save time series5. Make spectral density estimates of two longitudinal IFO
outputs (o1,o12)6. Convert these to acceleration (a1, adelta) and make spectral
density estimates7. Repeat for other noise sources8. Make correlated sum of all time-series
8.1.also convert this to acceleration and make spectral density estimates9. Make projection plots
34Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
SC x position noise
35Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
TM differential displacement noise
36Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
SC Residual Acceleration
37Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Differential Test-mass Acceleration
38Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Understanding the model
39Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Understanding the model
• Strategy:• get data out of the simulator at all possible points in the
loop• measure the (open-loop) transfer functions of individual
elements in the loop, wherever possible• non-linear blocks can’t be done this way• blocks which mix many inputs to one output need special
treatment• inspect the simulator code to validate the measurements
and expectation
39Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Understanding the model
• Strategy:• get data out of the simulator at all possible points in the
loop• measure the (open-loop) transfer functions of individual
elements in the loop, wherever possible• non-linear blocks can’t be done this way• blocks which mix many inputs to one output need special
treatment• inspect the simulator code to validate the measurements
and expectation• We focus only on the IFO signal loops as a first step
39Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Understanding the model
• Strategy:• get data out of the simulator at all possible points in the
loop• measure the (open-loop) transfer functions of individual
elements in the loop, wherever possible• non-linear blocks can’t be done this way• blocks which mix many inputs to one output need special
treatment• inspect the simulator code to validate the measurements
and expectation• We focus only on the IFO signal loops as a first step• Aim:
• validate and understand the model• use the model to develop our own model of LTP• produce new noise projections and compare
39Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Understanding the model
• Strategy:• get data out of the simulator at all possible points in the
loop• measure the (open-loop) transfer functions of individual
elements in the loop, wherever possible• non-linear blocks can’t be done this way• blocks which mix many inputs to one output need special
treatment• inspect the simulator code to validate the measurements
and expectation• We focus only on the IFO signal loops as a first step• Aim:
• validate and understand the model• use the model to develop our own model of LTP• produce new noise projections and compare
39Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Experiments
40
•Technical studies are broken down in to investigations
•These investigations are then packed in to the time-line
Measurement of Parasitic Voltages
Measurement of differential acceleration noise on LISA Pathfinder
Measurement of cross-talk between the y-axis and the x-axis on the LTP
Measurement of LTP dynamical coefficients by system identification
Analysis of Data from the Radiation Monitor on LISA Pathfinder
Thermal experiments on board the LTP
Magnetic experiments on board the LTP
OPD noise investigations for LTP
Laser frequency noise characterisation for LTP
Laser Amplitude Noise Characterisation for LTP
The Drift Mode for LISA Pathfinder
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Estimating external forces
•we want to estimate:• the residual acceleration on the spacecraft• the differential residual acceleration of the two test-
masses
41
o =�D · S�1 +C ·A ·Hdcpl ·H
��1 �gi +C ·A ·Hdcpl ·H · oi + gn +D · S�1 · orn
�
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Estimating external forces
•we want to estimate:• the residual acceleration on the spacecraft• the differential residual acceleration of the two test-
masses
41
o =�D · S�1 +C ·A ·Hdcpl ·H
��1 �gi +C ·A ·Hdcpl ·H · oi + gn +D · S�1 · orn
�control chain control chain
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Estimating external forces
•we want to estimate:• the residual acceleration on the spacecraft• the differential residual acceleration of the two test-
masses
41
o =�D · S�1 +C ·A ·Hdcpl ·H
��1 �gi +C ·A ·Hdcpl ·H · oi + gn +D · S�1 · orn
�
outputs input forces
guidance inputs
external forces readout noise
control chain control chain
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Estimating external forces
•we want to estimate:• the residual acceleration on the spacecraft• the differential residual acceleration of the two test-
masses
41
o =�D · S�1 +C ·A ·Hdcpl ·H
��1 �gi +C ·A ·Hdcpl ·H · oi + gn +D · S�1 · orn
�
outputs input forces
guidance inputs
external forces readout noise
control chain control chain
o =DS
�1
1 +G
(gi + gn) +G
1 +G
oi +1
1 +G
o
ro
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
1D system model
42
TM1
TM2
o12
o1
Cdf
Csus
x1
x2
ATAN
A
k1x1
k3x2
m
m
M
m1 = m2 = 1.96 kg
M = 475 kg
IFO/DMU
A1
A2 Asus
k1
k2
�A
�A
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
1D system model
42
o1
o12
�12
interferometer
1s2 + �2
3
1s2 + �2
1Cdf
Asus
Adf
SC Force
Noise
TM
Differential
Force
Noise
Sensing
Noise
Sensing
Noise
Csus
TM1
TM2
o12
o1
Cdf
Csus
x1
x2
ATAN
A
k1x1
k3x2
m
m
M
m1 = m2 = 1.96 kg
M = 475 kg
IFO/DMU
A1
A2 Asus
k1
k2
�A
�A
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
1D system model
42
o1
o12
�12
interferometer
1s2 + �2
3
1s2 + �2
1Cdf
Asus
Adf
SC Force
Noise
TM
Differential
Force
Noise
Sensing
Noise
Sensing
Noise
Csus
TM1
TM2
o12
o1
Cdf
Csus
x1
x2
ATAN
A
k1x1
k3x2
m
m
M
m1 = m2 = 1.96 kg
M = 475 kg
IFO/DMU
A1
A2 Asus
k1
k2
�A
�A
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
1D system model
42
o1
o12
�12
interferometer
1s2 + �2
3
1s2 + �2
1Cdf
Asus
Adf
SC Force
Noise
TM
Differential
Force
Noise
Sensing
Noise
Sensing
Noise
Csus
TM1
TM2
o12
o1
Cdf
Csus
x1
x2
ATAN
A
k1x1
k3x2
m
m
M
m1 = m2 = 1.96 kg
M = 475 kg
IFO/DMU
A1
A2 Asus
k1
k2
�A
�A
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Compute acceleration
43
⇥DS
�1 +C
⇤o = a = DS
�1
o
ro
+A+Coi
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Compute acceleration
43
Modelled
⇥DS
�1 +C
⇤o = a = DS
�1
o
ro
+A+Coi
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Compute acceleration
43
Modelled
Given⇥DS
�1 +C
⇤o = a = DS
�1
o
ro
+A+Coi
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Compute acceleration
43
Modelled
Given
Result
⇥DS
�1 +C
⇤o = a = DS
�1
o
ro
+A+Coi
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Compute acceleration
43
Modelled
Given
Result
Unknown⇥DS
�1 +C
⇤o = a = DS
�1
o
ro
+A+Coi
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Compute acceleration
43
Modelled
Given
Result
Unknown⇥DS
�1 +C
⇤o = a = DS
�1
o
ro
+A+Coi
Injections
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Compute acceleration
43
Modelled
Given
Result
Unknown⇥DS
�1 +C
⇤o = a = DS
�1
o
ro
+A+Coi
Injections
10−4 10−3 10−2 10−1 10010−14
10−13
10−12
10−11
10−10
10−9
10−8
10−7
Spectral Density [m][s−2 ][Hz−1/2]
Frequency [Hz]
DifferentialTM Force Noise
IFO Sensin
g Noise
Thruster Force Noise
a1
a1 ref
a2
a2 ref
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System identification
44Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System identification•Many optimisation steps require measurement of
physical parameters of the system
44Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System identification•Many optimisation steps require measurement of
physical parameters of the system•Dedicated experiments aim at determining small
sets of these physical parameters• e.g. stiffness of TM-SC coupling, IFO X-talk
44Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System identification•Many optimisation steps require measurement of
physical parameters of the system•Dedicated experiments aim at determining small
sets of these physical parameters• e.g. stiffness of TM-SC coupling, IFO X-talk
•Example: x-axis system identification• injecting signals in x-axis control loops we can
measure:• stiffness of the two test-masses, actuator gains, IFO X-talk,
loop delays
44Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
System identification•Many optimisation steps require measurement of
physical parameters of the system•Dedicated experiments aim at determining small
sets of these physical parameters• e.g. stiffness of TM-SC coupling, IFO X-talk
•Example: x-axis system identification• injecting signals in x-axis control loops we can
measure:• stiffness of the two test-masses, actuator gains, IFO X-talk,
loop delays
44Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Parameter estimation
•Employ different estimation methods to work with multiple inputs, multiple outputs
•All methods require a parametric model of the system
45Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Linear fit
46
Signal Data pre-processing
Compute whitening filtersWhiten data
Generate test signals
Generate system model
Linear fitCompute residuals
Noise Data pre-processing
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011 47Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Results using mission simulator
•We can simulate experiments using the mission simulator• 3D, non-linear, time-domain simulation
•We can recover the parameters with high precision• very dependent on having a good model
48Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LPF Mission Aims
49Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LPF Mission Aims
• Noise hunting and system optimisation to reach free-fall level of 3×10-14 ms-2 / √Hz• iterative process through experiments, system
identification, loop optimisation• reduce noise sources• reduce noise couplings
49Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
LPF Mission Aims
• Noise hunting and system optimisation to reach free-fall level of 3×10-14 ms-2 / √Hz• iterative process through experiments, system
identification, loop optimisation• reduce noise sources• reduce noise couplings
• Develop detailed system and noise model for LPF• goes hand in hand with system optimisation and
identification• will enable us to fully explain the observed residual
force noise• model can then be used to project the performance
towards LISA
49Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Requirements Differential Force Noise Budget
50
Total
Cap. Act
uN Thru
sters
OMS Sen
sing
Direct Forces
Star Tracker
OMS Angular
Spectral
Den
sity
[fms�
2/pHz]
Frequency [Hz]
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Improvements
•Due to the extensive ground testing campaigns we have a better understanding of the system• better noise models• more realistic coupling factors
51
Input from Bill Weber, UTN
From laboratory experiments to LISA Pathfinder: achieving LISA
geodesic motionCQG 28, 2011, 094002
LISA Pathfinder: an experimental analysis of the LPF free-fall
performanceBill Weber, Moriond Proceedings,
2011
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Other improvements
52
measured performance
+ lab environment
measured performance
system optimisation
system identification
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Best Estimate Differential Force Noise Budget
53
SpectralDensity
[fms�
2/pHz]
TotalCap. Act
uN ThrustersOMS Sensing
Direct Forces
Star Tracker
OMS Angular
Frequency [Hz]
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Free-flight experiment
54Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Free-flight experiment
•Capacitive actuation will be close to limiting around 1mHz
54Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Free-flight experiment
•Capacitive actuation will be close to limiting around 1mHz
•Do an experiment with the actuation off (more representative of a LISA-like configuration)• must be short otherwise the TM will drift too far• repeat many times
54Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Free-flight experiment
•Capacitive actuation will be close to limiting around 1mHz
•Do an experiment with the actuation off (more representative of a LISA-like configuration)• must be short otherwise the TM will drift too far• repeat many times
•Experiment:• kick test-mass away• turn off actuation• let the test-mass drift (in parabola)• repeat
54Wednesday, October 19, 11
M Hewitson, LPF, AMALDI, 15th July 2011
Simulation
55Wednesday, October 19, 11
M Hewitson, LPF, AMALDI, 15th July 2011
Data Analysis
•Need to analyse the multiple short (200s) drift segments to estimate the spectrum at 1mHz
•One approach: window the data, and proceed with normal PSD estimate
56Wednesday, October 19, 11
M Hewitson, LPF, AMALDI, 15th July 2011
PSD Estimate
57
Simulation with requirement-level noises but x3 reduced direct TM
force noise
Wednesday, October 19, 11
M Hewitson, LPF, AMALDI, 15th July 2011
Towards s/LISA/%s
58
Frequency [Hz]
SpectralDen
sity
[fms�
2/pHz]
Wednesday, October 19, 11
M Hewitson, LPF, AMALDI, 15th July 2011
Towards s/LISA/%s
58
Frequency [Hz]
SpectralDen
sity
[fms�
2/pHz]
Wednesday, October 19, 11
M Hewitson, LPF Data Analysis, GSFC, 11th October 2011
Conclusions
59
•Always expect the unexpected!• analysis tools need to be flexible and robust• modelling system needs to be highly configurable• operations science team needs to be well trained and
familiar with all aspects of the system•We will be ready through:
• on-ground unit- and system-level hardware testing• mock data challenges• operational exercises• training sessions• rigorous testing
Wednesday, October 19, 11
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