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

Abstract

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.

arXiv commentAccepted by MNRAS, 1 April 2021

References in corpus (9)

• Astrophysics Source Code Library
• A Search for Time Variation of the Fine Structure Constant
• Space-Time Variation of Physical Constants and Relativistic Corrections in Atoms
• Trident: a universal tool for generating synthetic absorption spectra from astrophysical simulations
• VoIgt profile Parameter Estimation Routine (VIPER): H I photoionization rate at z<0.5
• Artificial intelligence applied to the automatic analysis of absorption spectra. Objective measurement of the fine structure constant
• VoigtFit: A Python package for Voigt profile fitting
• Evaluating the New Automatic Method for the Analysis of Absorption Spectra Using Synthetic Spectra
• Getting the model right; an information criterion for spectroscopy

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• Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies
• Non-uniqueness in quasar absorption models and implications for measurements of the fine structure constant
• Getting the model right; an information criterion for spectroscopy
• Deep Forest: Neural Network reconstruction of the Lyman-alpha forest
• Precision in high resolution absorption line modelling, analytic Voigt derivatives, and optimisation methods
• Varying alpha, blinding, and bias in existing measurements
• Deep Forest: Neural Network reconstruction of intergalactic medium temperature
• The Mystery of Alpha and the Isotopes
• Sharpening quasar absorption lines with ESPRESSO: Temperature of warm gas at $z\sim 2$, constraints on the Mg isotopic ratio, and structure of cold gas at $z\sim 0.5$
• Fundamental physical constants at low and high redshifts
• Nature-inspired optimization, the Philippine Eagle, and cosmological parameter estimation
• Genetic algorithm demystified for cosmological parameter estimation