Bayesian parameter estimation in the second LISA Pathfinder mock data challenge

Nofrarias, M.; Röver, Christian; Hewitson, M.; Monsky, A.; Heinzel, Gerhard; Danzmann, K.; Ferraioli, L.; Hueller, M.; Vitale, S.

doi:10.1103/physrevd.82.122002

Cited by 22 publications

(33 citation statements)

References 22 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Ref [5,6] a frequentist approach to the problem was developed using both linear and non-linear parameter estimation techniques respectively. In Ref [7] a Bayesian framework was developed for the second LISA Pathfinder mock data challenge, based on a classic Metropolis-Hastings Markov Chain Monte Carlo (MCMC) scheme for parameter estimation [8,9]. From the point of view of the present paper, the method presented in Ref [7] is comparable with the algorithm working with the direct physical parameters and proposing the MCMC jumps from a multivariate Gaussian distribution.…”

Section: Introductionmentioning

confidence: 58%

“…Bayesian analysis has become a very useful tool in the field of space based gravitational waves [15][16][17][18][19][20][21][22][23][24][25][26][27], and has also been applied to LISA Pathfinder data analysis [7]. One of the advantages of Bayesian analysis is that it provides a direct method of calculating the marginalised posterior density function (PDF) for each of the parameters via Bayes' theorem where p(λ|s) is the posterior density function (PDF) for the solution λ given the data s(t), π(λ) denotes the prior probability of the parameters and p(s|λ) is the likelihood, which we will define below.…”

Section: Bayesian Analysis and Markov Chain Monte Carlomentioning

confidence: 99%

“…The problem of the calibration of parameters describing the dynamics was already discussed in previous works focusing on LISA Pathfinder [5][6][7]. In Ref [5,6] a frequentist approach to the problem was developed using both linear and non-linear parameter estimation techniques respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Bayesian analysis for LISA Pathfinder using an efficient Markov Chain Monte Carlo method

et al. 2014

View full text Add to dashboard Cite

We present a parameter estimation procedure based on a Bayesian framework by applying a Markov Chain Monte Carlo algorithm to the calibration of the dynamical parameters of the LISA Pathfinder satellite. The method is based on the Metropolis-Hastings algorithm and a two-stage annealing treatment in order to ensure an effective exploration of the parameter space at the beginning of the chain. We compare two versions of the algorithm with an application to a LISA Pathfinder data analysis problem. The two algorithms share the same heating strategy but with one moving in coordinate directions using proposals from a multivariate Gaussian distribution, while the other uses the natural logarithm of some parameters and proposes

show abstract

Section: Introductionmentioning

confidence: 58%

Section: Bayesian Analysis and Markov Chain Monte Carlomentioning

confidence: 99%

See 1 more Smart Citation

Improving Bayesian analysis for LISA Pathfinder using an efficient Markov Chain Monte Carlo method

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The B and C j coefficients, on the other hand, were estimated using a global parameter estimation method, which included all the available measurements for all the channels at the same time. A Markov Chain Monte Carlo (MCMC) method [18][19][20] was used as the parameter estimation technique. MCMC methods are advantageous over other techniques as they are straightforward to implement and allow the estimation for the mean value of the parameters and their posterior distribution.…”

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

Self Cite

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

“…To that end, dedicated experiments are going to be performed in order to estimate the unknown parameters of the system. And for that purpose, a number of parameter estimation methods and models of the LTP have been implemented [4][5][6]. The main question that arises is about the suitability of the different models implemented or, in simpler terms, which model can describe better the observations of the experiment.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model selection for LISA pathfinder

et al. 2014

View full text Add to dashboard Cite

The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the eLISA concept. The data analysis team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment onboard the LPF. These models are used for simulations, but, more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the data analysis team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching this problem is to recover the essential parameters of a LTP model fitting the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes factor between two competing models. In our analysis, we use three main different methods to estimate it: the reversible jump Markov chain Monte Carlo method, the Schwarz criterion, and the Laplace approximation. They are applied to simulated LPF experiments in which the most probable LTP model that explains the observations is recovered. The same type of analysis presented in this paper is expected to be followed during flight operations. Moreover, the correlation of the output of the aforementioned methods with the design of the experiment is explored.

show abstract

Bayesian parameter estimation in the second LISA Pathfinder mock data challenge

Cited by 22 publications

References 22 publications

Improving Bayesian analysis for LISA Pathfinder using an efficient Markov Chain Monte Carlo method

Improving Bayesian analysis for LISA Pathfinder using an efficient Markov Chain Monte Carlo method

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

Bayesian model selection for LISA pathfinder

Contact Info

Product

Resources

About