LISA Pathfinder pico-metres and femto-newtons in space

Transcription

1 LISA Pathfinder pico-metres and femto-newtons in space M Hewitson for the LPF team

2 The LPF Mission 2

3 The LPF Mission Technology demonstrator for a GW Observatory in Space (LISA-like design): micro-newton propulsion Gravitational Reference Sensor Interferometric techniques Drag-free control LPF will be placed in orbit around L1 Requirements relaxed compared to a typical space-borne GW observatory to make testing feasible: 1 order of magnitude in differential acceleration 1 order of magnitude higher frequency Two science payloads: LISA Technology Package (LTP) ST7 (NASA provided payload) 2

4 The LPF Mission Technology demonstrator for a GW Observatory in Space (LISA-like design): micro-newton propulsion Gravitational Reference Sensor Interferometric techniques Drag-free control measurement of residual differential acceleration of 30 fm s -2 / Hz at 1 mhz LPF will be between placed in two orbit free-falling around L1test-masses (pm accuracy position measurement) Requirements relaxed compared to a typical space-borne GW observatory to make testing feasible: 1 order of magnitude in differential acceleration 1 order of magnitude higher frequency Two science payloads: LISA Technology Package (LTP) ST7 (NASA provided payload) 2

5 The LPF Mission Technology demonstrator for a GW Observatory in Space (LISA-like design): micro-newton propulsion Gravitational Reference Sensor Interferometric techniques Drag-free control measurement of residual differential acceleration of 30 fm s -2 / Hz at 1 mhz LPF will be between placed in two orbit free-falling around L1test-masses (pm accuracy position measurement) Requirements relaxed compared to a typical space-borne GW observatory to make testing feasible: 1 order of magnitude in differential acceleration 1 order of magnitude higher frequency Two science payloads: LISA Technology Package (LTP) ST7 (NASA provided payload) 2

6 The LPF Mission Technology demonstrator for a GW Observatory in Space (LISA-like design): micro-newton propulsion Gravitational Reference Sensor Interferometric techniques Drag-free control measurement of residual differential acceleration of 30 fm s -2 / Hz at 1 mhz LPF will be between placed in two orbit free-falling around L1test-masses (pm accuracy position measurement) Requirements relaxed compared to a typical space-borne GW observatory to make testing feasible: 1 order of magnitude in differential acceleration 1 order of magnitude higher frequency Two science payloads: LISA Technology Package (LTP) ST7 (NASA provided payload) 2

7 Mission Operations We have about 90 days to achieve the goals characterise and optimise the system All days are filled with pre-planned experiments some flexibility is built-in Primary data analysis of the experiments will be done in real-time 3

8 Mission Operations We have about 90 days to achieve the goals characterise and optimise the system All days are filled with pre-planned experiments some flexibility is built-in Primary data analysis of the experiments will be done in real-time 3

9 The LPF team at the AEI Space Interferometry Group LPF Group Heather Audley Ingo Diepholz Karsten Danzmann Gerhard Heinzel Martin Hewitson Natalia Korsakova Jens Reiche Gudrun Wanner Andreas Wittchen... 4

10 The Core LPF DA Team Martin Hewitson Heather Audley Natalia Korsakova Ingo Diepholz Andreas Wittchen Mauro Hueller Valerio Ferroni Daniele Vetrugno Paolo Pivato Eric Plagnol Henri Inchauspe Luigi Ferraioli Michele Armano Miquel Nofrarias Ferran Gibert Karnesis Nikos 5

11 Data Analysis for LPF Analysis software and infrastructure Definition of experiments to be carried out in flight Simulation/testing of experiments Analysis pipelines End-to-end tests Interface to ESA s Science and Technology Operations Centre (STOC) 6

12 Analysis software and infrastructure 7

13 Analysis software and infrastructure ao('built-in'='retrieve_in_timespan', 'START TIME'= :00: LTPDA Toolbox UTC, 'NSECS'=86400, 'NAMES'= 'ADT20112', 'HOSTNAME'='lpsdas01.esac.esa.int', 'DATABASE'='STOC_Sim_2_ _Full', 'VERSION'='Version 1', 'CONN'= [], 'BINARY'=1, 'STOP TIME'= ' :00:00.000', 'TIME RANGE'=[], 'CONSTRAINTS'= '', 'FSTOL'= e-07, MATLAB toolbox which implements an object-oriented 'SORT'=1) data index('i'=1, 'J'=[]) analysis environment split('start_time'=' ao('built-in'='tm_parameters', 08:00:00', 'END_TIME'=' 'PARAMETER NAME'='EOM_TM2_M', 16:00:00') 'VERSION'='Initial version') objects track their history so results are traceable and rdivide(empty-plist) 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 split('offsets'=[ ]) ao('vals'= , 'DY'=[], 'AXIS'='y', 'N'=1, 'YUNITS'='', 'NAME'='', 'DESCRIPTION'= '') pzmodel('name'='', 'DESCRIPTION'= '', 'GAIN'=1, 'POLES'=[], 'ZEROS'=[], 'IUNITS'='', 'OUNITS'= '', 'DELAY'= ) fftfilt('npad'=[], 'FILTER'= '', 'INITIAL CONDITIONS'={} [0x0]) mtimes(empty-plist) setname('name'='s_cmd') split('offsets'=[ ]) ao('built-in'='retrieve_in_timespan', 'START TIME'= :00: UTC, 'NSECS'=86400, 'NAMES'= 'LAT10005', 'HOSTNAME'='lpsdas01.esac.esa.int', 'DATABASE'='STOC_Sim_2_ _Full', 'VERSION'='Version 1', 'CONN'= [], 'BINARY'=1, 'STOP TIME'= ' :00:00.000', 'TIME RANGE'=[], 'CONSTRAINTS'= '', 'FSTOL'= e-07, 'SORT'=1) index('i'=1, 'J'=[]) pzmodel('name'='', 'DESCRIPTION'= split('start_time'=' '', 'GAIN'=1, 'POLES'=[], 08:00:00', 'END_TIME'=' 'ZEROS'=[], 'IUNITS'='', 'OUNITS'= 16:00:00') '', 'DELAY'= ) ao('built-in'='retrieve_in_timespan', 'START TIME'= :00: UTC, 'NSECS'=86400, 'NAMES'= 'LAT10003', 'HOSTNAME'='lpsdas01.esac.esa.int', fftfilt('npad'=[], 'FILTER'= 'DATABASE'='STOC_Sim_2_ _Full', '', 'INITIAL CONDITIONS'={} 'VERSION'='Version 1', 'CONN'= [0x0]) [], 'BINARY'=1, 'STOP TIME'= ' :00:00.000', 'TIME RANGE'=[], 'CONSTRAINTS'= '', 'FSTOL'= e-07, 'SORT'=1) ao('vals'= e-06, diff('method'='3point', 'F0'= 'DY'=[], 'AXIS'='y', 'N'=1, '1/Nsecs', 'ORDER'='ZERO', split('offsets'=[ ]) index('i'=1, 'J'=[]) 'YUNITS'='s^-2', 'NAME'='', 'COEFF'=[]) 'DESCRIPTION'='') diff('method'='3point', 'F0'= split('start_time'=' '1/Nsecs', 'ORDER'='ZERO', mtimes(empty-plist) 08:00:00', 'END_TIME'=' 'COEFF'=[]) 16:00:00') fftfilt('npad'=[], 'FILTER'= setname('name'='diff acc') setname('name'='s_w2') '', 'INITIAL CONDITIONS'={} [0x0]) ao('vals'= e-07, 'DY'=[], 'AXIS'='y', 'N'=1, split('offsets'=[ ]) split('offsets'=[ ]) 'YUNITS'='s^-2', 'NAME'='', 'DESCRIPTION'='') split('offsets'=[ ]) mtimes(empty-plist) minus(empty-plist) split('offsets'=[ ]) setname('name'='s_dw') plus(empty-plist) split('offsets'=[ ]) plus(empty-plist) setname('name'='residual') END 7

14 Analysis software and infrastructure ao('built-in'='retrieve_in_timespan', 'START TIME'= :00: LTPDA Toolbox UTC, 'NSECS'=86400, 'NAMES'= 'ADT20112', 'HOSTNAME'='lpsdas01.esac.esa.int', 'DATABASE'='STOC_Sim_2_ _Full', 'VERSION'='Version 1', 'CONN'= [], 'BINARY'=1, 'STOP TIME'= ' :00:00.000', 'TIME RANGE'=[], 'CONSTRAINTS'= '', 'FSTOL'= e-07, MATLAB toolbox which implements an object-oriented 'SORT'=1) data index('i'=1, 'J'=[]) analysis environment split('start_time'=' ao('built-in'='tm_parameters', 08:00:00', 'END_TIME'=' 'PARAMETER NAME'='EOM_TM2_M', 16:00:00') 'VERSION'='Initial version') objects track their history so results are traceable and rdivide(empty-plist) 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 split('offsets'=[ ]) ao('vals'= , 'DY'=[], 'AXIS'='y', 'N'=1, 'YUNITS'='', 'NAME'='', 'DESCRIPTION'= '') pzmodel('name'='', 'DESCRIPTION'= '', 'GAIN'=1, 'POLES'=[], 'ZEROS'=[], 'IUNITS'='', 'OUNITS'= '', 'DELAY'= ) fftfilt('npad'=[], 'FILTER'= '', 'INITIAL CONDITIONS'={} [0x0]) mtimes(empty-plist) setname('name'='s_cmd') split('offsets'=[ ]) ao('built-in'='retrieve_in_timespan', 'START TIME'= :00: UTC, 'NSECS'=86400, 'NAMES'= 'LAT10005', 'HOSTNAME'='lpsdas01.esac.esa.int', 'DATABASE'='STOC_Sim_2_ _Full', 'VERSION'='Version 1', 'CONN'= [], 'BINARY'=1, 'STOP TIME'= ' :00:00.000', 'TIME RANGE'=[], 'CONSTRAINTS'= '', 'FSTOL'= e-07, 'SORT'=1) index('i'=1, 'J'=[]) pzmodel('name'='', 'DESCRIPTION'= split('start_time'=' '', 'GAIN'=1, 'POLES'=[], 08:00:00', 'END_TIME'=' 'ZEROS'=[], 'IUNITS'='', 'OUNITS'= 16:00:00') '', 'DELAY'= ) ao('built-in'='retrieve_in_timespan', 'START TIME'= :00: UTC, 'NSECS'=86400, 'NAMES'= 'LAT10003', 'HOSTNAME'='lpsdas01.esac.esa.int', fftfilt('npad'=[], 'FILTER'= 'DATABASE'='STOC_Sim_2_ _Full', '', 'INITIAL CONDITIONS'={} 'VERSION'='Version 1', 'CONN'= [0x0]) [], 'BINARY'=1, 'STOP TIME'= ' :00:00.000', 'TIME RANGE'=[], 'CONSTRAINTS'= '', 'FSTOL'= e-07, 'SORT'=1) ao('vals'= e-06, diff('method'='3point', 'F0'= 'DY'=[], 'AXIS'='y', 'N'=1, '1/Nsecs', 'ORDER'='ZERO', split('offsets'=[ ]) index('i'=1, 'J'=[]) 'YUNITS'='s^-2', 'NAME'='', 'COEFF'=[]) 'DESCRIPTION'='') diff('method'='3point', 'F0'= split('start_time'=' '1/Nsecs', 'ORDER'='ZERO', mtimes(empty-plist) 08:00:00', 'END_TIME'=' 'COEFF'=[]) 16:00:00') fftfilt('npad'=[], 'FILTER'= setname('name'='diff acc') setname('name'='s_w2') '', 'INITIAL CONDITIONS'={} [0x0]) ao('vals'= e-07, 'DY'=[], 'AXIS'='y', 'N'=1, split('offsets'=[ ]) split('offsets'=[ ]) 'YUNITS'='s^-2', 'NAME'='', 'DESCRIPTION'='') split('offsets'=[ ]) mtimes(empty-plist) minus(empty-plist) split('offsets'=[ ]) setname('name'='s_dw') plus(empty-plist) split('offsets'=[ ]) 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 plus(empty-plist) setname('name'='residual') END 7

15 Characterising the system Measurement of Parasitic Voltages Measurement of differential acceleration noise on LISA Pathfinder Measurement of cross-talks in the system 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 LTPDA 2.7.dev (R2013b Prerelease) :08: UTC ltpda: ecfe324 ose52_breakdown [m s 2 Hz 1/2 ] sqrt(lpsd(a12 (Reduced))) sqrt(lpsd(a12 (Solar))) sqrt(lpsd(a12 (Cold Gas))) sqrt(lpsd(a12 (AST))) sqrt(lpsd(a12 (SC Force Noise))) sqrt(lpsd(a12 (Reduced TM Force Noise))) sqrt(lpsd(a12 (Laser RO))) sqrt(lpsd(a12 (Cap Act))) sqrt(lpsd(a12 (Cap RO))) sqrt(a12 (Sum)) The Drift Mode for LISA Pathfinder Frequency [Hz] 8

16 Characterising the system Measurement of Parasitic Voltages Measurement of differential acceleration noise on LISA Pathfinder Measurement of cross-talks in the system Measurement of LTP dynamical coefficients by system identification Analysis of Data from the Radiation Monitor on LISA Pathfinder Modulate TM2 z electrodes with compensation voltage -100mV Modulate TM2 y electrodes with compensation voltage -100mV Modulate TM2 x electrodes with compensation voltage -100mV... 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 LTPDA 2.7.dev (R2013b Prerelease) :08: UTC ltpda: ecfe324 ose52_breakdown [m s 2 Hz 1/2 ] sqrt(lpsd(a12 (Reduced))) sqrt(lpsd(a12 (Solar))) sqrt(lpsd(a12 (Cold Gas))) sqrt(lpsd(a12 (AST))) sqrt(lpsd(a12 (SC Force Noise))) sqrt(lpsd(a12 (Reduced TM Force Noise))) sqrt(lpsd(a12 (Laser RO))) sqrt(lpsd(a12 (Cap Act))) sqrt(lpsd(a12 (Cap RO))) sqrt(a12 (Sum)) The Drift Mode for LISA Pathfinder Frequency [Hz] 8

17 Example: Charge measurement for TM2 Modulate x-axis electrodes of TM2 Coupling to force via charge on TM Force produces motion we can detect From detected motion we estimate test mass acceleration (hence the force) From the force we estimate the test mass charge q = ẍm tm C tot 4V 9

18 Charge Measurement Analysis Pipeline 10

19 Charge Measurement Analysis Pipeline Create pipeline Preprocess: resample to 1Hz, fix timing, etc Estimate residual differential test-mass acceleration Demodulate at the modulation frequency Scale to charge Estimate charge rate 5 qdpl = plist('names', 'Acc_LAT10005_Q', 'order', 1); a12_q_dot_fit = pipe.estimate_q_rate(qdpl); efit = pipe.evaluate_charge_rate(plist('names', 'Acc_LAT10005_Q')) 0 pipe = STOCQEstimate.STOCQEstimate(inv04021); objs = pipe.preprocess(); 1 ax12 = pipe.estimate_acc(plist('axis', 'o12')); filt = miir.lowpass(ax12.fs, 2e-3, 3); hpl = plist('frequencies', 5e-3,... 'bandwidths', 5e-3,... 'names', 'Acc_LAT10005',... 't0s', 101,... 'order', 1,... 'filters', filt); out = pipe.heterodyne(hpl); 3 vmod = ao(plist('vals', 0.4, 'yunits', 'V')); spl = plist(... 'frequencies', f0,... 'quadrature', 'sin',... 'names', 'Acc_LAT10005',... 'times', [ ],... 'mod axis', 'x2',... 'vmod', vmod); a12_q = pipe.scaleq(spl);

20 Charge Measurement Analysis Pipeline Create pipeline ẍm tm C tot q = 4V resample x mod 1Hz, fix timing, etc Estimate residual Scale to differential Q test-mass acceleration Demodulate at the modulation ~105 frequency e/s Scale to charge Estimate charge rate Estimate charge rate 5 qdpl = plist('names', 'Acc_LAT10005_Q', 'order', 1); a12_q_dot_fit = pipe.estimate_q_rate(qdpl); efit = pipe.evaluate_charge_rate(plist('names', 'Acc_LAT10005_Q')) 0 pipe = STOCQEstimate.STOCQEstimate(inv04021); Preprocess objs = pipe.preprocess(); 1 ax12 = pipe.estimate_acc(plist('axis', 'o12')); Estimate filt = miir.lowpass(ax12.fs, 2e-3, 3); hpl = plist('frequencies', 5e-3,... 'bandwidths', Acceleration 5e-3,... 'names', 'Acc_LAT10005',... 't0s', 101,... 'order', 1,... 'filters', filt); 3 out = pipe.heterodyne(hpl); vmod = ao(plist('vals', 0.4, 'yunits', 'V')); spl = plist(... 'frequencies', f0,... 'quadrature', 'sin',... 'names', Demodulate 'Acc_LAT10005',... 'times', [ ],... 'mod axis', 'x2',... 'vmod', vmod); 4 a12_q = pipe.scaleq(spl); 2 10

21 Available Pipelines/steps Package Step Description STOCCommon Pipeline class Preprocess Resample and split +STOCCommon Heterodyne Demodulate at set of frequencies in both quadratures +STOCEstimateAcc +STOCQEstimate Polyfit EvaluatePolyFit STOCEstimateAcc EstimateAcceleration STOCQEstimate ScaleQ STOCVcompScan CheckElectrodes Fit polynomial to given set of parameters Produce data sets from polyfit coefficients Pipeline class Estimate residual acceleration for different DOFs Package class Scale acceleration estimates to charge Pipeline class Produces plots of commanded dc voltages on all sets of electrodes +STOCVcompScan AverageSegments Produces average values of data in given segments +STOCDriftModeWindowed FormVcompVsAcc ScaleV STOCDriftModeWindowed ComputeInloopAcc ReplaceKicks CheckHistogram BuildWindows ApplyWindows FitContributions ComputeResiduals Forms a data set of compensation voltage versus acceleration Scale acceleration estimates to Voltage Pipeline class Compute the in-loop acceleration of TM2 along x Replace detected force kicks with zeros Make a histogram of non-zero data Build high and low frequency windows based on acceleration data Apply the windows to the chosen telemetry Fit different signal contributions by minimising PSD Compute the residual acc noise of TM2 accounting for the different contributions 11

22 Available Pipelines/steps Package Step Description STOCCommon Pipeline class Preprocess Resample and split +STOCCommon Heterodyne Demodulate at set of frequencies in both quadratures +STOCEstimateAcc +STOCQEstimate Polyfit EvaluatePolyFit STOCEstimateAcc EstimateAcceleration STOCQEstimate ScaleQ STOCVcompScan CheckElectrodes Fit polynomial to given set of parameters Produce data sets from polyfit coefficients Pipeline class Estimate residual acceleration for different DOFs Package class Scale acceleration estimates to charge Pipeline class Produces plots of commanded dc voltages on all sets of electrodes +STOCVcompScan AverageSegments Produces average values of data in given segments +STOCDriftModeWindowed FormVcompVsAcc ScaleV STOCDriftModeWindowed ComputeInloopAcc ReplaceKicks CheckHistogram BuildWindows ApplyWindows FitContributions ComputeResiduals Forms a data set of compensation voltage versus acceleration Scale acceleration estimates to Voltage Pipeline class Compute the in-loop acceleration of TM2 along x Replace detected force kicks with zeros Make a histogram of non-zero data Build high and low frequency windows based on acceleration data Apply the windows to the chosen telemetry Fit different signal contributions by minimising PSD And more... quicklook, sys id Compute the residual acc noise of TM2 accounting for the different contributions 11

23 Preparing for operations 12

24 Preparing for operations What s required? Define the experiments to perform on-orbit Translate experiments into operational investigations Prepare analysis pipeline for each investigation Train the team LTPDA Training Sessions LPF Schools 12

25 Preparing for operations What s required? Define the experiments to perform on-orbit Translate experiments into operational investigations Prepare analysis pipeline for each investigation Train the team LTPDA Training Sessions LPF Schools ESA s STOC (Science and Technology Operations Centre) Exercises Defining and testing the operation investigations Simulations Simulate segments of the mission timeline Bring teams to operational environment to analyse the data in real-time 12

26 Flight hardware test campaigns Various test campaigns have taken place... The LPF DA team and the AEI are involved in many of these OMS EM Campaign OMS FM Campaign SC Closed-loop tests On-Station Thermal Tests We have a wealth of real data to analyse 13

27 Flight hardware test campaigns Various Longitudinal test campaigns have taken place... The LPF DA team and the AEI are involved in many of these OMS EM Campaign pm/ Hz OMS FM Campaign SC Closed-loop tests On-Station Thermal Tests Angular nrad/ Hz We have a wealth of real data to analyse 13

28 From requirements to expectations... h fm s 2 / p Hz Spectral Density i Direct Forces Star Tracker Requirements Total Thrusters Frequency [mhz] OMS Sensing Electrostatic Actuation Residual Differential Test-mass Acceleration 14

29 Free-flight experiment h fm s 2 / p Hz Spectral Density i Star Tracker Requirements Total Direct Forces Thrusters Frequency [mhz] OMS Sensing Electrostatic Actuation Residual Differential Test-mass Acceleration 15

30 In context... i fm s 2 / p Hz LPF Mission Requirements elisa Mission Goal LPF Performance (Subsystem Requirements) LPF Performance (Best Estimate) LPF Performance (Free-Flight) Spectral Density h Frequency [mhz] 16

31 LPF Hardware Status (in brief) 17

32 LPF Hardware Status (in brief) Flight optical bench delivered 17

33 LPF Hardware Status (in brief) Flight optical bench delivered Flight test masses delivered 17

34 LPF Hardware Status (in brief) Flight optical bench delivered Flight test masses delivered Caging mechanism delivered 17

35 LPF Hardware Status (in brief) Flight optical bench delivered Flight test masses delivered Caging mechanism delivered Laser and electronics on SC 17

36 LPF Hardware Status (in brief) Flight optical bench delivered SC Module is ready to integrate payload. Flight test masses delivered Caging mechanism delivered Laser and electronics on SC 17

37 LPF Hardware Status (in brief) Flight optical bench delivered Flight test masses delivered SC Module is ready to integrate payload. Redesigned electrode housing well on track. Both flight units to be delivered Oct 1st. Caging mechanism delivered Laser and electronics on SC 17

38 LPF Hardware Status (in brief) Flight optical bench delivered Flight test masses delivered Caging mechanism delivered SC Module is ready to integrate payload. Redesigned electrode housing well on track. Both flight units to be delivered Oct 1st. Cold Gas thrusters: off the shelf, flying on Gaia Laser and electronics on SC 17

39 LPF Hardware Status (in brief) Flight optical bench delivered Flight test masses delivered Caging mechanism delivered SC Module is ready to integrate payload. Redesigned electrode housing well on track. Both flight units to be delivered Oct 1st. Cold Gas thrusters: off the shelf, flying on Gaia Laser and electronics on SC Well on-track for July 15 launch 17

40 Testing alternative theories of gravity with LPF ~v X Z 0 d Z SP Y Y 18

41 Testing alternative theories of gravity with LPF Enough fuel left to manoeuvre from L1 to the Earth-Sun gravitational saddle point ideal for testing deviations from Newtonian gravity Velocity of LPF will put signal exactly in mhz band Answer the questions: What can we say if we see no signal? for a given theory, what area of parameter space can we rule out? What can we say if we see a signal? estimate parameter values for different theories Data analysis studies to assess the effect of trajectory uncertainties on the expected signal Y Z ~v SP 0 d X Y Z 18

42 Testing alternative theories of gravity with LPF Enough fuel left to manoeuvre from L1 to the Earth-Sun gravitational saddle point ideal for testing deviations from Newtonian gravity Velocity of LPF will put signal exactly in mhz band Answer the questions: What can we say if we see no signal? for a given theory, what area of parameter space can we rule out? What can we say if we see a signal? estimate parameter values for different theories Data analysis studies to assess the effect of trajectory uncertainties on the expected signal Bayesean analysis for parameter estimation for a given theory currently focussing on non-relativistic TeVeS (MOND-like theory) numerical simulations to generate expected gravitational potential for given parameter set fly through the potential on a particular trajectory and extract signal as seen by LPF from the signal, estimate theory parameters Y Z ~v SP 0 d X Y Z 18

43 Future activities 19

44 Future activities Develop the LTP lab further to support mission operations Continue to define and implement the mission time-line and associated analyses and tools Prepare to support and participate (heavily) in LPF operations Continue work on the science case and development of an LPF extended mission for a saddle point fly-by 19