JWST Observations of Starbursts: Massive Star Clusters in the Central Starburst of M82

Abstract We present a near-infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses >10 4 M ⊙ . Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of ≈1 pc and stellar masses up to 10 6 M ⊙ . By comparing the color–color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A V ∼ 3−24 mag, corresponding to A 2.5 μ m ∼ 0.3−2.1 mag. There is still appreciable dust extinction toward these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is β = 1.9 ± 0.2, in excellent agreement with studies of star clusters in other galaxies.

Authors

Publication Details

Journal
The Astrophysical Journal Letters
Published
2024-09-27
DOI
https://doi.org/10.3847/2041-8213/ad7af3
Citations
21
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
6.96

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

JWST Observations of Starbursts: Massive Star Clusters in the Central Starburst of M82

Y. D. Mayya, David S. Meier, Fabian Walter, Ilse De Looze et al.
21 citations
The Astrophysical Journal Letters
Galaxies: Formation, Evolution, Phenomena
6.96
article

JWST Observations of Starbursts: Massive Star Clusters in the Central Starburst of M82

Y. D. Mayya, David S. Meier, Fabian Walter, Ilse De Looze, Martha L. Boyer, M. Relaño, K. L. Emig, Rebecca C. Levy, Sylvain Veilleux, Vicente Villanueva, Rodrigo Herrera-Camus, Simon C. O. Glover, David B. Fisher, Laura Lenkić, J. Ott, Leindert Boogaard, Elisabeth A. C. Mills, Ralf S. Klessen, Alberto D. Bolatto, Adam K. Leroy, Elizabeth Tarantino, Bolivia Cuevas-Otahola, P. van der Werf, Serena A. Cronin, María J. Jiménez-Donaire, Daniel A. Dale, Keaton Donaghue, Torsten Böker
article en
21 citations

Abstract

Abstract We present a near-infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses >10 4 M ⊙ . Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of ≈1 pc and stellar masses up to 10 6 M ⊙ . By comparing the color–color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A V ∼ 3−24 mag, corresponding to A 2.5 μ m ∼ 0.3−2.1 mag. There is still appreciable dust extinction toward these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is β = 1.9 ± 0.2, in excellent agreement with studies of star clusters in other galaxies.

The Astrophysical Journal LettersVol. 973(2)
National Science Foundation, National Aeronautics and Space Administration, California Institute of Technology, Space Telescope Science Institute, Ministerio de Ciencia e Innovación, Agencia Nacional de Investigación y Desarrollo, Jet Propulsion Laboratory, Agencia Estatal de Investigación
Openalex Percentile: Top 3%
Galaxies: Formation, Evolution, Phenomena
6.96
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.