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Missing data are a common problem in experimental and observational physics. They can be caused by various sources, either an instruments saturation, or a contamination from an external event, or a data loss. In particular, they can have a disastrous effect when one is seeking to characterize a colored-noise-dominated signal in Fourier space, since they create a spectral leakage that can artificially increase the noise. It is therefore important to either take them into account or to correct for them prior to e.g. a Least-Square fit of the signal to be characterized. In this paper, we present an application of the {it inpainting} algorithm to mock MICROSCOPE data; {it inpainting} is based on a sparsity assumption, and has already been used in various astrophysical contexts; MICROSCOPE is a French Space Agency mission, whose launch is expected in 2016, that aims to test the Weak Equivalence Principle down to the $10^{-15}$ level. We then explore the {it inpainting} dependence on the number of gaps and the total fraction of missing values. We show that, in a worst-case scenario, after reconstructing missing values with {it inpainting}, a Least-Square fit may allow us to significantly measure a $1.1times10^{-15}$ Equivalence Principle violation signal, which is sufficiently close to the MICROSCOPE requirements to implement {it inpainting} in the official MICROSCOPE data processing and analysis pipeline. Together with the previously published KARMA method, {it inpainting} will then allow us to independently characterize and cross-check an Equivalence Principle violation signal detection down to the $10^{-15}$ level.
The MICROSCOPE space mission, launched on April 25, 2016, aims to test the weak equivalence principle (WEP) with a 10^-15 precision. To reach this performance requires an accurate and robust data analysis method, especially since the possible WEP vio
The analysis of physical measurements often copes with highly correlated noises and interruptions caused by outliers, saturation events or transmission losses. We assess the impact of missing data on the performance of linear regression analysis invo
In this contribution to the CPT19 proceedings, we summarize efforts that use data from the MICROSCOPE space mission to search for Lorentz violation in the Standard-Model Extension.
We use data from the T-SAGE instrument on board the MICROSCOPE space mission to search for Lorentz violation in matter-gravity couplings as described by the Lorentz violating Standard-Model Extension (SME) coefficients $(bar{a}_text{eff})_mu^w$, wher
We present a Gaussian regression method for time series with missing data and stationary residuals of unknown power spectral density (PSD). The missing data are efficiently estimated by their conditional expectation as in universal Kriging, based on