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A general purpose fitting model for one-dimensional astrometric signals is developed, building on a maximum likelihood framework, and its performance is evaluated by simulation over a set of realistic image instances. The fit quality is analysed as a function of the number of terms used for signal expansion, and of astrometric error, rather than RMS discrepancy with respect to the input signal. The tuning of the function basis to the statistical characteristics of the signal ensemble is discussed. The fit sensitivity to a priori knowledge of the source spectra is addressed. Some implications of the current results on calibration and data reduction aspects are discussed, in particular with respect to Gaia.
A tool for representation of the one-dimensional astrometric signal of Gaia is described and investigated in terms of fit discrepancy and astrometric performance with respect to number of parameters required. The proposed basis function is based on t
We provide a basis to select the optimal algorithm according to the specific observational conditions in ground-based astrometry, and clarify the loss of precision in the case of not achieving optimum. The principle of the centering algorithms based
We present the Exoplanet Simple Orbit Fitting Toolbox (ExoSOFT), a new, open-source suite to fit the orbital elements of planetary or stellar mass companions to any combination of radial velocity and astrometric data. To explore the parameter space o
We introduce the program MAVKA for determination of characteristics of extrema using observations in the adjacent data intervals, with intended applications to variable stars, but it may be used for signals of arbitrary nature. We have used a dozen o
Hypothesis Selection is a fundamental distribution learning problem where given a comparator-class $Q={q_1,ldots, q_n}$ of distributions, and a sampling access to an unknown target distribution $p$, the goal is to output a distribution $q$ such that