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

## TL;DR

This paper evaluates a new AI-based method that combines three numerical techniques to analyze synthetic absorption spectra, accurately recovering velocity structures and variations in fundamental constants, aiming to improve large-scale spectral analysis.

## Contribution

It introduces a novel AI method unifying GVPFIT, VPFIT, and BMA, and demonstrates its effectiveness using synthetic spectra for the first time.

## Key findings

- Accurately recovers velocity structures.
- Estimates variation in the fine structure constant.
- Proves effectiveness of synthetic spectra in analysis.

## Abstract

We recently presented a new "artificial intelligence" method for the analysis of high-resolution absorption spectra (Bainbridge and Webb, Mon. Not. R. Astron. Soc. 2017, 468,1639-1670). This new method unifies three established numerical methods: a genetic algorithm (GVPFIT); non-linear least-squares optimisation with parameter constraints (VPFIT); and Bayesian Model Averaging (BMA). In this work, we investigate the performance of GVPFIT and BMA over a broad range of velocity structures using synthetic spectra. We found that this new method recovers the velocity structures of the absorption systems and accurately estimates variation in the fine structure constant. Studies such as this one are required to evaluate this new method before it can be applied to the analysis of large sets of absorption spectra. This is the first time that a sample of synthetic spectra has been utilised to investigate the analysis of absorption spectra. Probing the variation of nature's fundamental constants (such as the fine structure constant), through the analysis of absorption spectra, is one of the most direct ways of testing the universality of physical laws. This "artificial intelligence" method provides a way to avoid the main limiting factor, i.e., human interaction, in the analysis of absorption spectra.