Artificial intelligence and quasar absorption system modelling;
  application to fundamental constants at high redshift
  Exploring the possibility that fundamental constants of Nature might vary
temporally or spatially constitutes one of the key science drivers for the
European Southern Observatory's ESPRESSO spectrograph on the VLT and for the
HIRES spectrograph on the ELT. High-resolution spectra of quasar absorption
systems permit accurate measurements of fundamental constants out to high
redshifts. The quality of new data demands completely objective and
reproducible methods. We have developed a new fully automated Artificial
Intelligence-based method capable of deriving optimal models of even the most
complex absorption systems known. The AI structure is built around VPFIT, a
well-developed and extensively-tested non-linear least-squares code. The new
method forms a sophisticated parallelised system, eliminating human
decision-making and hence bias. Here we describe the workings of such a system
and apply it to synthetic spectra, in doing so establishing methods of
importance for future analyses of VLT and ELT data. The results show that
modelling line broadening for high-redshift absorption components should
include both thermal and turbulent components. Failing to do so means it is
easy to derive the wrong model and hence incorrect parameter estimates. We also
argue that model non-uniqueness can be significant, such that it is not
feasible to expect to derive an unambiguous estimate of the fine structure
constant alpha from one or a small number of measurements. No matter how
optimal the modelling method, it is a fundamental requirement to use a large
sample of measurements to meaningfully constrain temporal or spatial alpha
variation.
