High-mass stars are one of the main drivers that shape the galaxy. Understanding the process through which they form is therefore of the utmost importance. This process, however, is not yet fully understood. Contributing to this field, the ALMAGAL survey has studied over 6000 star-forming regions with a higher resolution than any other survey before. On one hand, this data will help scientists study these obscure regions and draw a clearer picture of the high-mass star-formation process. On the other hand, the sheer volume and complexity of the data produced by this survey is far too great for conventional methods to handle swiftly.

This thesis therefore explores the use of unsupervised machine learning (ML) methods to cluster astrochemical spectra from the ALMAGAL survey. The aim of this thesis is to explore which models are best suited for the task, and to use the resulting clusters to establish a chemical evolutionary sequence for high-mass star-forming regions.....

https://github.com/ javialonso05/MSc-Thesis