The youngest star clusters in the Large Magellanic Cloud

ABSTRACT This study presents a comprehensive analysis of the youngest stellar clusters in the Large Magellanic Cloud, utilizing a multiwavelength approach. We analyse data spanning from infrared to ultraviolet wavelengths, with the goal of enhancing our understanding of these clusters’ physical properties, such as age, mass, and size. Our methodology includes a novel cluster detection procedure; it employs machine learning techniques for the accurate identification of these young clusters. The Markov Chain Monte Carlo analysis, using the Automated Stellar Cluster Analysis tool, plays a crucial role in deriving the clusters’ key physical parameters. Our findings provide significant insights into the early stages of stellar and galactic evolution, particularly in dwarf galaxy environments, and contribute to the broader understanding of star formation and cluster evolution. For the 109 clusters in our sample younger than 5 Myr, we measure a positive correlation between cluster mass and the mass of the most massive star in the cluster. This study emphasizes the importance of multiwavelength observations in revealing the intricate properties of young stellar clusters.

Authors

Institutions

Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2025-03-12
DOI
https://doi.org/10.1093/mnras/staf413
Citations
2
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
2.31
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The youngest star clusters in the Large Magellanic Cloud

Ricardo Chávez, Rosa A. González‐Lópezlira, Gustavo Bruzual
2 citations
Monthly Notices of the Royal Astronomical Society
Stellar, planetary, and galactic studies
2.31
article

The youngest star clusters in the Large Magellanic Cloud

Ricardo Chávez, Rosa A. González‐Lópezlira, Gustavo Bruzual
article en
2 citations

Abstract

ABSTRACT This study presents a comprehensive analysis of the youngest stellar clusters in the Large Magellanic Cloud, utilizing a multiwavelength approach. We analyse data spanning from infrared to ultraviolet wavelengths, with the goal of enhancing our understanding of these clusters’ physical properties, such as age, mass, and size. Our methodology includes a novel cluster detection procedure; it employs machine learning techniques for the accurate identification of these young clusters. The Markov Chain Monte Carlo analysis, using the Automated Stellar Cluster Analysis tool, plays a crucial role in deriving the clusters’ key physical parameters. Our findings provide significant insights into the early stages of stellar and galactic evolution, particularly in dwarf galaxy environments, and contribute to the broader understanding of star formation and cluster evolution. For the 109 clusters in our sample younger than 5 Myr, we measure a positive correlation between cluster mass and the mass of the most massive star in the cluster. This study emphasizes the importance of multiwavelength observations in revealing the intricate properties of young stellar clusters.

Monthly Notices of the Royal Astronomical SocietyVol. 538(4)
Secretaría de Ciencia Tecnología e Innovación (MX), Universidad Nacional Autónoma de México (MX)
Openalex Percentile: Top 13%
Stellar, planetary, and galactic studies
2.31
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.