Artificial intelligence applied to the automatic analysis of absorption spectra. Objective measurement of the fine structure constant.

Abstract

A new and automated method is presented for the analysis of high-resolution absorption spectra. Three established numerical methods are unified into one "artificial intelligence" process: a genetic algorithm (GVPFIT); non-linear least-squares with parameter constraints (VPFIT); and Bayesian Model Averaging (BMA).The method has broad application but here we apply it specifically to the problem of measuring the fine structure constant at high redshift. For this we need objectivity and reproducibility. GVPFIT is also motivated by the importance of obtaining a large statistical sample of measurements of ∆α/α. Interactive analyses are both time consuming and complex and automation makes obtaining a large sample feasible.In contrast to previous methodologies, we use BMA to derive results using a large set of models and show that this procedure is more robust than a human picking a single preferred model since BMA avoids the systematic uncertainties associated with model choice.Numerical simulations provide stringent tests of the whole process and we show using both real and simulated spectra that the unified automated fitting procedure out-performs a human interactive analysis. The method should be invaluable in the context of future instrumentation like ESPRESSO on the VLT and indeed future ELTs.We apply the method to the z abs = 1.8389 absorber towards the z em = 2.145 quasar J110325-264515. The derived constraint of ∆α/α = 3.3±2.9×10 −6 is consistent with no variation and also consistent with the tentative spatial variation reported in Webb et al. (2011) andKing et al. (2012).

Evaluating the New Automatic Method for the Analysis of Absorption Spectra Using Synthetic Spectra

“…We believe that it is unlikely that a human interactively fitting this set of synthetic spectra would perform better than GVPFIT. Figure 4 shows interesting characteristics in the evolution of ∆α/α, similar to those seen by Bainbridge and Webb [1] in the real spectra of the z abs = 1.839 absorption system towards J110325-264515. There appears to be an underlying linear trend in the evolution of ∆α/α, with occasional conspicuous departures (see Figure 4).…”

“…These figures show the residuals are well behaved and there are no discrepancies between the data and the model. The BMA model is determined by summing over all models for each pixel in the data, with the contribution of each model being weighted by its relative likelihood using AICc (using Equations (7) and (13) from [1]):…”

“…Since the new method presented in Bainbridge and Webb (2017) [1] removes the previously required human interaction, we can begin to analyse the ever-increasing number of quasar echelle spectra more efficiently and undertake projects that were previously unrealistic. One example of this is modelling both thermally and turbulently broadened models for each absorption system independently, allowing a more reliable comparison between models and data.…”

An Updated Constraint on Variations of the Fine-Structure Constant Using Wavelengths of Fe II Absorption Line Multiplets

“…The precision regarding the relative change in αachieved from our results is the most stringent limit in comparison with the results produced by Quast et al (2004) [15]. Our results also possess order of magnitude improvements compared to the exact results from Chand et al (2006) [16] and are better than the results derived by Levshakov et al (2006; [17][18][19], Molaro et al (2005; [14][15][16][17][18][19][20][21][22][23][24][25], Murphy et al (2008;2014) [27,34], and Bainbridge et al (2017) [40]. A considerable advance has been made in improving the laboratory wavelengths of transitions in the Fe II multiplet line that can be utilized in our analyses.…”

The status of varying constants: a review of the physics, searches and implications

“…Finally, genetic algorithms are being used to develop automated analysis pipelines [66], which should lead to significantly faster (and possibly also more objective) processing of the data.…”