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Exploring the possibility that fundamental constants of Nature might varytemporally or spatially constitutes one of the key science drivers for theEuropean Southern Observatory's ESPRESSO spectrograph on the VLT and for theHIRES spectrograph on the ELT. High-resolution spectra of quasar absorptionsystems permit accurate measurements of fundamental constants out to highredshifts. The quality of new data demands completely objective andreproducible methods. We have developed a new fully automated ArtificialIntelligence-based method capable of deriving optimal models of even the mostcomplex absorption systems known. The AI structure is built around VPFIT, awell-developed and extensively-tested non-linear least-squares code. The newmethod forms a sophisticated parallelised system, eliminating humandecision-making and hence bias. Here we describe the workings of such a systemand apply it to synthetic spectra, in doing so establishing methods ofimportance for future analyses of VLT and ELT data. The results show thatmodelling line broadening for high-redshift absorption components shouldinclude both thermal and turbulent components. Failing to do so means it iseasy to derive the wrong model and hence incorrect parameter estimates. We alsoargue that model non-uniqueness can be significant, such that it is notfeasible to expect to derive an unambiguous estimate of the fine structureconstant alpha from one or a small number of measurements. No matter howoptimal the modelling method, it is a fundamental requirement to use a largesample of measurements to meaningfully constrain temporal or spatial alphavariation.

Artificial intelligence and quasar absorption system modelling; application to fundamental constants at high redshift