Data Analysis for the LISA Technology Package Technology Package M Hewitson for the LTP Team 1. ... M Hewitson, LTP Data Analysis, COSPAR, 2008 Introducing LTPDA MATLAB Toolbox implements

Data Analysis for the

LISA Technology Package

M Hewitson for the LTP Team

1

M Hewitson, LTP Data Analysis, COSPAR, 2008

Contents

Introduction Requirements and goals Analysis environment Experiment master plan Mock Data Challenges

2


The Core Team

3

Martin HewitsonAnneke MonskyMiquel NofrariasIngo Diepholz

Mauro HuellerLuigi FerraioliNicola Tateo

Josep Sanjaun

Adrien Grynagier

Andrea Mattioli

Michele Armano

+ many other people helping


Mission Goal

Test technology for LISA Measure the external forces acting on a freely falling test-mass

interferometrically measure relative position of two test-masses

4

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


DA goal

Characterise various subsystems Extract maximum possible science from the data Analyse a set of pre-defined experiments

Experimental Master Plan (EMP) Perform quasi-real-time analysis

feed-forward to following experiments5


Requirements

Need a robust and flexible DA environment commercial software (reduce testing overhead)

Easy to use mission scientists need not be programming experts

A sufficient and well tested set of tools to carry out the planned analyses Traceable and reproducible results A team of scientists who can use the tools to carry out the analyses

6


Some challenges

Very limited data download rate [15kbit/s]

Careful, optimised design of experiments Many experiments, finite mission time

Minimise experiment length Interesting features are on the time-scales of the experiments

experiments last a few hours 7


Additional challenges

Complicated satellite... Signals are sampled in various un-synchronised subsystems

1/600 Hz up to a few Hz demanding pre-processing of data

Many loops / subsystems to characterise

Want to measure external force noise on free-falling test-mass

8


Typical activities

9

Spectral EstimatesPSD, Coherence,

CPSD, TF

Parameter EstimatesSignal extraction

model fitting

Noise budgetTransfer functions

noise sourcesprojection

Noise subtractionTime-series pre-processing

Digital filtering

CalibrationsConversion to

Extract physical quantities

xSimulation

Template fittingDynamical responses

Analytical models


Analysis Environment

Choices: commercial software accountable, reproducible results

graphical interface for analysis design and execution Multi-user concurrent data access

10

MATLAB

Analysis Objects

SIMULINK

Client/Server system


What’s an AO?

Not results:

11

Analysis Object

Numerical

Data

Provenance

Processing

history

Additional

meta-data

- creator

- date

- IP address

- Hostname

- Operating System

- software versions

- Name

- ID number

- Comment

- pipeline file(s)

- Name

- Algorithm version

- Parameter list

- Creation date/time

- Input histories

- Name

- Numerical data

vectors

- Creation date/time

- Additional flags


Tracking history

12

traceable results

Input

AO

Output

AO(s)

Input

AO

Algorithmic step

input

history

input

history

Algorithm

history

Intelligent algorithms


Introducing LTPDA

MATLAB Toolbox implements the AO/history concept object-oriented approach to data analysis fully integrated with MATLAB graphical design and execution of data analysis pipelines

13

+----------------------------------------------------+| || || **** || ** || ------------- || //// / \\\\ || /// / \\\ || | / | || ** | +----+ / +----+ | ** || ***| | |//-------| | |*** || ** | +----+ /+----+ | ** || | / | || \\\ / /// || \\\\ // //// || ------------- || ** || **** || || Welcome to LTPDA Toolbox || || Version: 1.01 || Release: (R2008a) || Date: 16-06-08 || |+----------------------------------------------------+

~2500 source files

~100,000 lines of code


LTPDA Objects

14

timetimespan

pzmodel

plist

miir mfir specwinpole zero

param

tsdatafsdataxydata cdata

provenance

history

ao User Classes


Intelligent algorithms

15

b = foo(a,pl)

output AOs input AOs input parameters

Input

AO

Output

AO(s)

Input

AO

Algorithmic step

input

history

input

history

Algorithm

history


(Some) AO methods

16

Spectralpwelchtfecoherecpsdfft/ifftdft

lpsdltfelcoherelcpsd

Pre-processingresampledownsampleupsampledelaydetrendzeropad

selectsplitfilterwhitensmoothfind

Operators+ - * / .* .^ ./ ^ abs, transposesin, cos, tan, exp, ln, log, log10 min, max, mean, median, mode, std, varnorm, phase, real, imag sign, sort, sqrt, sum, uminus


Visualising dataSince AOs contain more than just data, nice plots are easy to make

17

>> iplot(a5)


How history works

18

ao

name

history

version

data

name

input histories

version

params

name

input histories

version

params

name

input histories

version

params


Reliving history

19

ao

plot history

function a_out = test % TEST.M % % % written by ao2m / $Id: ao2m.m,v 1.11 2007/11/14 16:30:18 ingo Exp $% % based on analysis object:% name: a3 - 10 / ((Data) + (Data)) - (Data)% provenance: created by hewitson@localhost[192.168.2.104] on MACI/7.5 (R2007b)/0.7 (R2007b) at 2008-01-06% description: % original m-file: % % a7331931 = ao(plist([param('VALS', [10]) ]));a7331814 = ao(plist([param('VALS', [[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 a7331773 = ao(plist([param('VALS', [10]) ]));a7331865 = plus(a7331773, a7331814);a7331960 = minus(a7331865, a7331931);a_out = a7331960;

recreate script


Storing objects

Store in ASCII format (long-life) XML format for all LTPDA objects

20

p = plist('a', 2, 'b', 'hello')

<?xml version="1.0" encoding="utf-8"?><ltpda_object> <object shape="1x1" type="plist"> <property prop_name="name" shape="1x12" type="char">plist object</property> <property prop_name="params" shape="1x2" type="param"> <object shape="1x2" type="param"> <property prop_name="name" shape="1x1" type="char">A</property> <property prop_name="key" shape="1x1" type="char">A</property> <property prop_name="val" shape="1x1" type="double">2 </property> <property prop_name="created" shape="1x1" type="time"> <object shape="1x1" type="time"> <property prop_name="name" shape="1x11" type="char">time-object</property> <property prop_name="utc_epoch_milli" shape="1x1" type="double">1204637764923 </property> <property prop_name="timezone" shape="1x1" type="sun.util.calendar.ZoneInfo">UTC</property> <property prop_name="timeformat" shape="1x1" type="timeformat"> <object shape="1x1" type="timeformat"> <property prop_name="format_str" shape="1x23" type="char">yyyy-mm-dd HH:MM:SS.FFF</property> <property prop_name="format_nr" shape="1x1" type="double">-1 </property>


Graphical DA

Drag-and-drop analysis construction Use SIMULINK as drawing tool Analysis pipeline is then executed by underlying LTPDA functions GUI implements no additional analysis functionality All class functionality is

21


Data AccessCentralised data storage Remote client access to data Submit and retrieve objects from within MATLAB

22

MATLAB

MATLAB

MATLAB

convert to AOs

MySQL ServerRaw data


Searching for objectsMission will create ~20000 AOs Need meta-data to find these again multiple tables store meta-data about each object object code (XML) stored in table virtual collections for objects

23

idhashxml

objsidobj_idobj_typenamecreatedversioniphostnameossubmittedcomment1comment2comment3comment4comment5comment6validatedvdate

objmeta

idobj_iduser_idtransdatedirection

transactionsidnobjsobj_ids

collectionsidfirstnamefamilynameusernameemailtelephoneinstitution

users

idobj_iddata_typedata_idmfilenamemdlfilename

aoidobj_idin_filefs

miir/mfir

idxunitsyunitsfsnsecst0

tsdataidxunitsyunitsfs

fsdataidxunitsyunits

cdataidxunitsyunits

xydata


Testing

Results produced by analysis affect mission time-line must be well tested!

24

System Tests

Test overall system (pipelines)

Unit Tests

Test basic building blocks

Acceptance Tests

Integrate into operations -

perform mission


Release schedule

25

devbegins V1.0

0.1 ... 0.5 ... 0.9

V2.0

1.1 ... 1.3

V3.0

2007 2008 2009

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

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


EMP

Mission consists of ~90 runs each run contains at least one experiment ~200 experiments will be designed

many similar with varying conditions Collection of Technical Notes feed in to the EMP

26


Development cycle

27

EMP

ExperimentDesign Notes

Mock Data Challenges

Lab experience

Hardware Design and

Testing


RIN Noise Projection

Need a full noise budget to explain residual force noise we measure

e.g. photon pressure noise from amplitude fluctuations of laser require a RIN of about 1 part in 105 to keep below at 1 mHz

28

s =2P

m c�2

1 pm/�

Hz

additional: calibrate photodiodes on-orbit


ProcedureInject a modulation signal to laser

large enough to dominate differential

29o12

modulation signal

P


ProcedureConvert differential displacement (o12) to out-of-loop acceleration

TF from laser amplitude fluctuations to acceleration is flat

30o12

modulation signal

P


ProcedureMeasure the transfer function from RIN (P) to differential acceleration (a12) at injection frequency Convert P(t) to equivalent force

31

R12(t) = P (t)� |T |


Numerical example

32

1D LTP Simulation

o1(t)o12(t)P (t)


Analysis pipeline

33


Results

34

10 100 10001

Frequency [mHz]

100

Dis

plac

emen

t� pm

s�2/�

Hz⇥

0.1

0.001

1

0.01

10

1000

0.0001

a1a2Rx


Results: excess RIN

35

100

Dis

plac

emen

t� pm

s�2/�

Hz⇥

0.1

10

1000

1

10 100 10001

Frequency [mHz]

a1a2Rx


Mock Data Challenges

We need Mock Data Challenges (MDCs) to:

gain understanding of the system drive tool development test tools train Scientists verify experiments in the EMP

36


How an MDC works

37

Produce data sets based on 1

+ input noise models

2

3

Compare results to those

expected

4

Define MDC model(s),

assumptions, etc

1

Analyse data (based on some details from 1)


MDC1

38

o12o1

x1 x12

TM1 is drag-freeSC follows TM1TM2 follows TM1


MDC1 - Aims

Begin the process! Recover the (out-of-loop) differential force noise on TMs from synthetic data Exercise the LTPDA toolbox

39

Test-mass Force noise

Thruster Force noise

10 pm s�2/�

Hz100 nN/

�Hz

Sensing noise

5 pm/�

Hz

1mHz 1mHz


[DS�1 + C]⌅o = a = DS�1⌅on + ⌅A

Equations of motion

40

Dynamics

Sensing Control

MeasurementsNoise Sources


Calibration

Convert displacement measurements to acceleration

41

Filter

a = [DS�1 + C]⇤o


Interpretation

42

Frequency [mHz]

Forc

ePer

Uni

tM

ass

� ms�

2/�

Hz⇥

10�16

10�14

10�12

10�10

10�8

10�6

0.1 1 10 100 1000

�a1a�1⇥

Forc

ePer

Uni

tM

ass

� ms�

2/�

Hz⇥

10�16

10�14

10�12

10�10

10�8

10�6

Frequency [mHz]0.1 1 10 100 1000

�a�a��⇥

SC force noise

IFO Sensing Noise

TM force noise


Results

43

10-4

10-3

10-2

10-1

100

10-14

10-13

10-12

10-11

10-10

10-9

10-8

Frequency [Hz]

Am

plitu

de

[ms�

2/�

Hz]

a∆ averaged

error

a∆ predicted

Frequency [mHz]1 10 100 10000.1

1

10

100

1000

0.1

Forc

ePer

Uni

tM

ass

� pms�

2/�

Hz⇥

ExpectedAverage±�

�a2a�2⇥

SensingNoise

TM Force

noise

Using 11 data sets


Results 2

10-4

10-3

10-2

10-1

100

10-10

10-9

10-8

10-7

10-6

Frequency [Hz]

Am

plitu

de

[ms�

2/�

Hz]

a1 averaged

error

a1 predicted

44Frequency [mHz]1 10 100 10000.1

1

10

100

0.1

ExpectedAverage±�

Forc

ePer

Uni

tM

ass

� nms�

2/�

Hz⇥

�a1a�1⇥

Thruster Force

NoiseSensing

Noise


Summary

Data analysis for LTP is under heavy development

Analysis infrastructure is well developed Additional algorithms and testing needed

lab use, MDCs, formal testing Experiment analysis design is underway

MDCs, Technical notes, lab tests, ...

45

Documents

Data Analysis for the LISA Technology Package Technology Package M Hewitson for the LTP Team 1. ... M Hewitson, LTP Data Analysis, COSPAR, 2008 Introducing LTPDA MATLAB Toolbox implements