7.3.3 Processing steps
The modelfitting process follows the following steps (decribed in detail in Holl et al. (2023b)):

1.
A period search is first performed by each module. Given the nature of the astrometric dataset, the application of standard tools for the periodogram analysis of unevenly sampled timeseries (e.g. the Generalized LombScargle periodogram, Zechmeister and Kürster 2009) is not possible. For any given source, both the DEMCMC and GA modules draw a large sample of initial trial periods for sinusoidal signals projected along the scan directions of the timeseries. For DEMCMC this is a uniform grid up to twice the observations time span. For GA this is random period sample uniformly drawn in log10(period) between roughly the shorted and longest timeintervals in the observations as derived from repeated bootstrap sampling of the observations. One may note that when running the GA algorithm it is allowed to explore periods up to twice the observations time span through its mutation operators. For both DEMCMC and GA, a sparsely sampled selection of periods corresponding to local ${\chi}^{2}$ minima becomes the seed for the initialisation of the DEMCMC chains and GA genome population;

2.
Each algorithm is allowed to determine its bestfit parameters for a maximum of 60 s of singlethreaded wallclock CPU computation time.

3.
Finally, the bestfitting model parameters are determined by evaluating the Bayesian Information Criterion (Schwarz 1978) metric: $\mathrm{BIC}=k\mathrm{ln}(n)2\mathrm{ln}\mathcal{L}$ (where $k$ is the number of parameters estimated by the model and $n$ is the number of data points). The solution with the lowest BIC is selected as the bestfit solution. The published solution therefore originates from either the DEMCMC or GA approaches, but the information on which was chosen for a given source is not published;

4.
Symmetric estimates of the parameter uncertainties are obtained by reconstructing the covariance matrix directly from the Jacobians of all parameters for all observations.