LISA Pathfinder data analysis

Antonucci, F.; Armano, M.; Audley, H.; Auger, G.; Benedetti, M.; Binétruy, P.; Boatella, C.; Bogenstahl, J.; Bortoluzzi, D.; Bosetti, Paolo; Caleno, M.; Cavalleri, A.; Cesa, M.; Chmeissani, M.; Ciani, G.; Conchillo, A.; Congedo, G.; Cristofolini, I.; Cruise, M.; Danzmann, K.; Marchi, Fabrizio De; Diaz‐Aguiló, Marc; Diepholz, I.; Dixon, G. J.; Dolesi, R.; Fauste, Jorge; Ferraioli, L.; Fertin, D.; Fichter, Walter; Fitzsimons, E.; Freschi, M.; Marín, A. F. García; Marirrodriga, C. García; Gesa, L.; Giardini, Domenico; Grimani, C.; Grynagier, A.; Guillaume, Bernard; Guzmán, F.; Harrison, I.; Heinzel, Gerhard; Hewitson, M.; Hollington, D.; Hough, J.; Hoyland, D.; Hueller, M.; Huesler, J.; Jeannin, Olivier; Jennrich, O.; Jetzer, Philippe; Johlander, B.; Killow, C. J.; Llamas, Xavier; Lloro, I.; Lobo, Alberto; Maarschalkerweerd, R.; Madden, S.; Mance, D.; Mateos, I.; McNamara, P. W.; Mendes, José F. G.; Mitchell, E.; Monsky, A.; Nicolini, Davide; Nicolodi, Daniele; Nofrarias, M.; Pedersen, Finn; Perreur-Lloyd, M.; Perreca, A.; Plagnol, E.; Prat, P.; Racca, Giuseppe D.; Raïs, B.; Ramos‐Castro, J.; Reiche, J.; Pérez, José Antonio; Robertson, D. I.; Rozemeijer, H.; Sanjuán, Jose; Schulte, M.; Shaul, D.; Stagnaro, L.; Strandmoe, S.; Steier, Frank; Sumner, T. J.; Taylor, A.; Texier, D.; Trenkel, C.; Tombolato, D.; Vitale, S.; Wanner, Gudrun; Ward, H.; Waschke, S.; Wass, Peter; Weber, William J.; Zweifel, Peter

doi:10.1088/0264-9381/28/9/094006

Cited by 21 publications

(21 citation statements)

References 17 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data were cut into overlapping segments of 4 × 10 4 seconds and windowed with a minimum 4-sample BlackmanHarris window [23]. All data were processed with LTPDA [24,25]. The lowest four data bins were discarded as they are affected by a systematic bias due to the window spectral leakage.…”

Section: B Analysis Of Datamentioning

confidence: 99%

Capacitive sensing of test mass motion with nanometer precision over millimeter-wide sensing gaps for space-borne gravitational reference sensors

Armano¹,

Audley²,

Auger³

et al. 2017

Phys. Rev. D

View full text Add to dashboard Cite

Capacitive sensing of test mass motion with nanometer precision over millimeter-wide sensing gaps for space-borne gravitational reference sensors. Physical Review D, 96(6), 062004.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/148989/ We report on the performance of the capacitive gap-sensing system of the Gravitational Reference Sensor (GRS) onboard the LISA Pathfinder (LPF) spacecraft. From in-flight measurements, the system has demonstrated a performance, down to 1 mHz, that is ranging between 0.7 aF Hz −1/2 and 1.8 aF Hz −1/2 . That translates into a sensing noise of the test mass motion within 1.2 nm Hz −1/2 and 2.4 nm Hz −1/2 in displacement and within 83 nrad Hz −1/2 and 170 nrad Hz −1/2 in rotation. This matches the performance goals for LPF and it allows the successful implementation of the gravitational waves observatory LISA. A 1/f tail has been observed for frequencies below 1 mHz, the tail has been investigated in detail with dedicated in-flight measurements and a model is presented in the paper. A projection of such noise to frequencies below 0.1 mHz shows that an improvement of performance at those frequencies is desirable for the next generation of GRS sensors for space-borne gravitational waves observation. 2PACS numbers: 04.80. Nn, 95.55.Ym

show abstract

Section: B Analysis Of Datamentioning

confidence: 99%

Capacitive sensing of test mass motion with nanometer precision over millimeter-wide sensing gaps for space-borne gravitational reference sensors

Armano¹,

Audley²,

Auger³

et al. 2017

Phys. Rev. D

View full text Add to dashboard Cite

show abstract

“…If the stiffness, k, can be determined through calibration, then the measured position of TM 1 relative to the spacecraft, x 1S , can be used to estimate the stiffness-related force, a s = k x 1S , and subtract it in the data analysis. Procedures for estimating the stiffness to high precision have been developed for LISA Pathfinder Antonucci (2011), which require measuring the motion of one test mass relative to another. This is possible for LPF since the LPF spacecraft houses two test masses and the relative acceleration noise between the two is very low (3 × 10 −14 m s −2 Hz −1/2 ).…”

Section: Stiffness-related Noisementioning

confidence: 99%

“…Determination of such coefficients has been precisely demonstrated by the GOCE mission (Frommknecht 2011) and will also be performed during the LISA Pathfinder mission (Antonucci 2011).…”

Section: Actuation Cross-couplingmentioning

confidence: 99%

Drift mode accelerometry for spaceborne gravity measurements

Conklin

2015

J Geod

View full text Add to dashboard Cite

A drift mode accelerometer is a precision instrument for spacecraft that overcomes much of the acceleration noise and readout dynamic range limitations of traditional electrostatic accelerometers. It has the potential of achieving acceleration noise performance similar to that of dragfree systems over a restricted frequency band without the need for external drag-free control or continuous spacecraft propulsion. Like traditional accelerometers, the drift mode accelerometer contains a high-density test mass surrounded by an electrode housing, which can control and sense all six degrees of freedom of the test mass. Unlike traditional accelerometers, the suspension system is operated with a low duty cycle so that the limiting suspension force noise only acts over brief, known time intervals, which can be neglected in the data analysis. The readout is performed using a laser interferometer which is immune to the dynamic range limitations of even the best voltage references typically used to determine the inertial acceleration of electrostatic accelerometers. The drift mode accelerometer is a novel offshoot of the like-named operational mode of the LISA Pathfinder spacecraft, in which its test mass suspension system is cycled on and off to estimate the acceleration noise associated with the front-end electronics. This paper presents the concept of a drift mode accelerometer, describes the operation of such a device, develops models for its performance with respect to non-drag-free satellite geodesy and gravitational wave missions, and discusses plans for testing the performance of a prototype sensor in the laboratory using torsion pendula.

show abstract

“…We expect then to be able to understand the largest part of LPF results within a linear model, and to have to deal with non linearities only as occasional small deviations from the linear regime. The simulator is proving extremely useful for testing this approach and the related data analysis algorithms and tools [14].…”

Section: B Application To Lpf Simulator Data Analysismentioning

confidence: 99%

Data series subtraction with unknown and unmodeled background noise

et al. 2014

View full text Add to dashboard Cite

LISA Pathfinder (LPF), ESA's precursor mission to a gravitational wave observatory, will measure the degree to which two test-masses can be put into free-fall, aiming to demonstrate a residual relative acceleration with a power spectral density (PSD) below 30 fm/s$^2$/Hz$^{1/2}$ around 1 mHz. In LPF data analysis, the measured relative acceleration data series must be fit to other various measured time series data. This fitting is required in different experiments, from system identification of the test mass and satellite dynamics to the subtraction of noise contributions from measured known disturbances. In all cases, the background noise, described by the PSD of the fit residuals, is expected to be coloured, requiring that we perform such fits in the frequency domain. This PSD is unknown {\it a priori}, and a high accuracy estimate of this residual acceleration noise is an essential output of our analysis. In this paper we present a fitting method based on Bayesian parameter estimation with an unknown frequency-dependent background noise. The method uses noise marginalisation in connection with averaged Welch's periodograms to achieve unbiased parameter estimation, together with a consistent, non-parametric estimate of the residual PSD. Additionally, we find that the method is equivalent to some implementations of iteratively re-weighted least-squares fitting. We have tested the method both on simulated data of known PSD, and to analyze differential acceleration from several experiments with the LISA Pathfinder end-to-end mission simulator

show abstract

LISA Pathfinder data analysis

Cited by 21 publications

References 17 publications

Capacitive sensing of test mass motion with nanometer precision over millimeter-wide sensing gaps for space-borne gravitational reference sensors

Capacitive sensing of test mass motion with nanometer precision over millimeter-wide sensing gaps for space-borne gravitational reference sensors

Drift mode accelerometry for spaceborne gravity measurements

Data series subtraction with unknown and unmodeled background noise

Contact Info

Product

Resources

About