Identifying Evolutionary Stages of Molecular Clumps through Unsupervised and Supervised Machine Learning

TL;DR

This study demonstrates that machine learning can effectively classify molecular clumps into evolutionary stages using astrochemical data, offering a data-driven alternative to traditional, often ambiguous, classification methods.

Contribution

The paper introduces a combined unsupervised and supervised machine learning approach to classify molecular clumps, improving classification accuracy and handling ambiguous cases in star formation studies.

Findings

Unsupervised clustering reveals distinct evolutionary groups based on molecular line intensities.

Supervised learning classifies previously uncertain sources, mainly as non-active star formation regions.

Infrared properties are not significant features for classification, indicating similar envelope characteristics across different masses.

Abstract

The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to ambiguous signatures, distance uncertainties or heavily obscured IR emission, a significant fraction of sources often remains unclassified. This work demonstrates the capability of machine learning (ML) as a complementary, data-driven approach to automate the identification and classification of these clumps using data from the MALT90 survey, complemented by Spitzer IR photometry. We applied unsupervised clustering with HDBSCAN on molecular line intensities, revealing distinct groupings that correspond to evolutionary stages. Using only five molecular lines (HCO$^+$, HNC, N$_2$H$^+$, HCN, C$_2$H), we identified stable clusters of protostars and regions without active star formation, driven primarily by C$_2$H and N$_2$H$^+$ emission. Incorporating H$^{13}$CO$^+$ gave rise to a distinct UV-dominant cluster, tracing more evolved regions. Infrared properties appeared as non-significant features implying that envelopes of clumps with different masses are similar in their global infrared characteristics. We then employed supervised learning to classify clumps with previously uncertain categories and provided classifications for 522 objects, predominantly as regions without active star formation. Our results show that ML techniques can effectively uncover intrinsic evolutionary structures in complex astrochemical data and assign categories to uncertain sources, providing a powerful, data-driven complement to traditional methods.