2.4.6 Bias and astrophysical background determination
Author(s): Nigel Hambly
As mentioned previously in
Section 2.3.5 concerning bias, on-ground
monitoring of the electronic offset levels is enabled via prescan telemetry that
arrives in one-second bursts approximately once per hour per device. The processing chain simply
analyses these data by recording robust mean and dispersion measures for each
burst for each device, and low-order spline interpolation is employed to provide
model offset levels at arbitrary times when processing samples from the CCDs.
Regarding the offset non-uniformities mentioned previously, the effects are
stable on timescales of many months so periodic recalibration takes place every 3
to 4 months and the most recent calibration available for any given processing
period is used to correct those features in the data (Section 1.3.3 and Table 1.8). Further details are given
in Fabricius et al. (2016) and Hambly et al. (2018).
The approach to modelling the ‘large-scale’
background is to use high-priority observations to measure a two-dimensional
background surface independently for each device so that model values can be
provided at arbitrary along-scan times and across-scan positions during
downstream processing (e.g., when making astrometric and photometric
measurements from all science windows). A combination of empty windows (VOs; Section 1.1.3)
and a subset of leading/trailing samples from faint star windows are
used as the input data to a linear least-squares determination of the spline
surface coefficients. The procedure is iterative to enable outlier rejection
of those samples adversely affected by prompt-particle events (commonly
known as ‘cosmic rays’) and other perturbing phenomena. For numerical robustness,
the least-squares implementation employs Householder
decomposition (van Leeuwen 2007) for the matrix manipulations.
Some example large-scale astrophysical background models are illustrated in
Following the large-scale background determination, a set of residuals
for a subset of the calibrating data are saved temporarily for use
downstream in the charge release calibration process.
Residuals folded by distance from last charge injection are analysed by
determining the robust mean value and formal error on that value in each
TDI line after the injection. The across-scan injection profile, also determined
in a one day calibration employing empty windows that happen to lie over
injection lines, is used to factor out the power-law dependency of release
signal versus injection level. Note that in this way new calibrations of the
charge injection profile and the charge release signature are produced for
each processing period. This is done to follow the assumed slow evolution in their characteristics
as on-chip radiation damage accumulates (Section 1.3.3).
Figure 2.12 shows some example charge
release curves typical of those during the Gaia DR2 observation period.
Example across-scan charge injection profiles
are shown in Figure 2.13.