The Gaia measurement principle is that differences in the transit time between stars observed
by each telescope can be translated into angular measurements (see Gaia Collaboration et al.2016 for details). All these measurements are affected
if the basic angle (the angle between the two telescopes, ; Section 1.1.3) varies with time. Either the angle needs to be ultra-stable, or its variations be known to the mission accuracy level (1 as).
Gaia is largely self-calibrating (i.e., all calibration parameters are estimated from the science
observations; see Gaia Collaboration et al.2016 for details). Low-frequency variations of the basic angle (, with h) can
be fully eliminated by self-calibration. High-frequency random variations are
also not a concern because they are averaged during all transits. However,
intermediate-frequency variations are difficult to eliminate by
self-calibration, especially if they are synchronised with the spacecraft spin
phase, such that the residuals can introduce systematic errors (biases) in the astrometric
results (Michalik and Lindegren 2016; Butkevich et al.2017). As a result, intermediate-frequency changes of the basic angle need to be monitored by
The BAM device (Section 1.1.3) is continuously measuring differential
changes in the basic angle. It basically generates one fixed, artificial star
per telescope by introducing two collimated laser beams into each primary
mirror (see Figure 2.9). The BAM is composed of two
optical benches in charge of producing the interference pattern for each
telescope. A number of optical fibres, polarisers, beam splitters, and mirrors
are used to generate all four beams from one common light source. See
Gaia Collaboration et al. (2016) and Gielesen et al. (2012) for further details. Each Gaia telescope then
generates an image on the same, dedicated BAM CCD, which is an interference pattern due to the coherent input
light source. The relative along-scan displacement between the two fringe
patterns is a direct measurement of the basic-angle variations.
A detailed description of the BAM data model, the data collection, fitting, and daily processing is outlined in Section 7 of Fabricius et al. (2016).