Object

Title: Metallicity Effects on Machine Learning Classification of Dusty Stellar Sources in the Magellanic Clouds

Journal or Publication Title:

Բյուրականի աստղադիտարանի հաղորդումներ: Երևանի աստղադիտարանի բյուլետեն = Сообщения Бюраканской обсерватории. Бюллетень ереванской Астрономической обсерватории = Contributions from the Burakan observatory. Bulletin of the Erevan astronomical observatory

Date of publication:

2025

Volume:

72

Number:

2

ISSN:

2579-2276

Coverage:

390-395

Abstract:

Differences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects the performance of machine learning models in classifying dusty stellar sources. The five stellar classes studied include young stellar objects (YSOs), red supergiants (RSGs), post-asymptotic giant branch stars (PAGBs), and oxygenand carbon-rich asymptotic giant branch stars (OAGBs and CAGBs), which are key phases of stellar evolution involved in dust production. Using spectroscopically labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, we trained and evaluated a probabilistic random forest (PRF) classifier with four approaches: (1) separate training on LMC and SMC, including all five classes, (2) excluding the underpopulated PAGB class, (3) combined LMC and SMC datasets, and (4) cross-galaxy training and testing. The model achieved 93% accuracy on the SMC and 88% on the LMC across all five classes. In the SMC, PAGB sources were misclassified as YSOs, mainly because of their small sample size (4 objects). When PAGB was excluded, both the LMC and the SMC reached 92% accuracy. A combined dataset produced the same accuracy, and cross-galaxy training yielded similar results, indicating that metallicity does not significantly impact model performance. A comparison of absolute CMDs for the LMC and SMC confirms their similarity in stellar populations. These findings suggest that environmental metallicity has little effect on ML-based classification of dusty stellar sources, supporting the use of combined datasets and cross-galaxy models in low-metallicity environments.

Format:

pdf

Identifier:

oai:arar.sci.am:426398

General note:

Communications of the Byurakan Astrophysical Observatory (ComBAO) is a peer-reviewed scientific journal, which publishes research in observational and theoretical astronomy/astrophysics and presents recent advances in these fields. It is being published by the NAS RA V. Ambartsumian Byurakan Astrophysical Observatory (BAO) in English in electronic form. The journal publishes original papers, review papers, brief reports, book reviews, special communications, observational and theoretical results in various fields of astronomy and related sciences, and some editorial notes, including anniversaries and obituaries. Under the heading “Legacy”, the renewed magazine will republish some old articles of high value in English. The heading “Guest articles” will bring to the attention of readers the articles of researchers who are not employees of BAO. ComBAO was founded in 1946 and regularly published in 1946-1990. However, the publication was interrupted because of the economic situation after the disintegration of the Soviet Union in 1991.

Location of original object:

ՀՀ ԳԱԱ Հիմնարար գիտական գրադարան

Object collections:

Last modified:

Feb 4, 2026

In our library since:

Feb 3, 2026

Number of object content hits:

9

All available object's versions:

https://arar.sci.am/publication/459244

Show description in RDF format:

RDF

Show description in OAI-PMH format:

OAI-PMH

Objects

Similar

This page uses 'cookies'. More information