Speaker
Description
Nowadays, the use of high-sensitivity astronomical facilities such as the Atacama Large Millimeter/submillimeter Array (ALMA) has opened important applications of data science for the detection of new species in the interstellar medium. However, tools for analyzing and interpreting these complex datasets have not yet reached their full potential. The increasing availability of observational repositories offers a valuable opportunity to overcome the limitations of traditional analytical techniques. Therefore, the development of new tools capable of efficiently exploiting this vast amount of information and AI capabilities has become essential. This is the main objective of the project Artificial Intelligence Integral Tool for Astrochemical Analysis (AI-ITACA). In this work, we present an approach of Machine Learning-based generative models that allows automatic analysis of the gas-phase emission spectra from radio astronomy observations. The trained models perform the retrieval of molecular parameters by comparison of a repository of local candidates generated with two components in the ML training: 1) Synthetic spectra characterized by logarithmic column density, excitation temperature, velocity, and full width at half maximum under LTE conditions. 2) Observational residual features from a complete survey in ALMA Band 3, which setup covers the full spectral range of the band. First, we performed an identification of best practices for spectral feature extraction using local data representations for the selected species. This was achieved through the application of decision-tree algorithms and neural networks (NN) for the frequency regions of interest and hyper-parameter optimization. The analysis was conducted using both observational data from the G31.41+0.31 Unbiased ALMA Spectral Observational (GUAPOS) Survey and a repository of molecular synthetic spectra. Second, we carried out parameter characterization on new observational data to generate parametric inference maps. Each component was independently analyzed to build specialized inference models, maximizing the performance of the generative framework. The methodology was then applied to spectral observations of additional sources and compared with molecular parameters obtained through classical analysis techniques. The tool is deployed online, providing a user interface for spectral dataset analysis and intuitive visualization of results. This methodology presents an application of ML techniques, which is important to digest the large volume of data provided by the current, and especially, by the new generation of astronomical facilities like the ALMA Wideband Sensitivity Upgrade (WSU).