Pattern finding in millimetre-wave spectra of massive young stellar objects

¹ Department of Astrophysics/IMAPP, Radboud University, PO Box 9010, 6500 GL Nijmegen, The Netherlands
² Department of Space, Earth & Environment, Chalmers University of Technology, 412 93 Gothenburg, Sweden
³ FACom, Instituto de Física – FCEN, Universidad de Antioquia, Calle 70 No. 52-21, Medellín, Colombia
⁴ School of Physics and Astronomy, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK
⁵ SKA Observatory, Jodrell Bank, Lower Withington, Macclesfield SK11 9FT, UK
⁶ Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy, School Of Natural Science, The University of Manchester, Manchester M13 9PL, UK
⁷ Ghana Space Science and Technology Institute, Accra, Ghana

^★ Corresponding author; y.angarita@astro.ru.nl; yenifer.angarita@chalmers.se

Received: 30 August 2024
Accepted: 23 December 2024

Abstract

Massive stars (M_* > 8 M_⊙) play a pivotal role in shaping their galactic surroundings due to their high luminosity and intense ionizing radiation. However, the precise mechanisms governing the formation of massive stars remain elusive. Complex organic molecules (COMs) offer an avenue for studying star formation across the low- to high-mass spectrum because COMs are found in every young stellar object (YSO) phase and offer insight into the structure and temperature. We aim to unveil patterns in the evolution of COM chemistry in 41 massive young stellar objects (MYSOs) sourced from diverse catalogues, using Atacama Large Millimeter/Submillimeter Array Band 6 spectra. Previous line analysis of these sources revealed the presence of methanol, methyl acetylene, and methyl cyanide with diverse excitation temperatures (a few tens to hundreds of Kelvin) and column densities (spanning two to four orders of magnitude in range), indicating a possible evolutionary path across sources. However, such analyses usually involve manual line extraction and rotational diagram fitting. We improved upon this process by directly retrieving the physicochemical state of MYSOs from their dimensionally reduced spectra. We used a locally linear embedding to find a lower-dimensional projection for the physicochemical parameters obtained from individual line analysis. We identified clusters of similar MYSOs in the embedded space using a Gaussian mixture model, revealing three groups of MYSOs corresponding to distinct physicochemical conditions: (i) cold, COM-poor sources, (ii) warm, medium-COM-abundance sources, and (iii) hot, COM-rich sources. Principal component analysis (PCA) of the source spectra further supported an evolutionary path across MYSO groups. Finally, by training a simple random forest model on the first few PCA components, we found that the physicochemical state of MYSOs in our sample can be derived directly from the spectra. Our results highlight the effectiveness of dimensionality reduction in obtaining clear physical insights directly from MYSO spectra.