PAH Marks the Spot: Digging for Buried Clusters in Nearby Star-forming Galaxies

Abstract The joint capabilities of the Hubble Space Telescope (HST) and JWST allow for an unparalleled look at the early lives of star clusters at near- and mid-infrared wavelengths. We present here a multiband analysis of embedded young stellar clusters in 11 nearby, star-forming galaxies, using the PHANGS-JWST and PHANGS-HST data sets. We use the Zooniverse citizen science platform to conduct an initial by-eye search for embedded clusters in near-UV/optical/near-infrared images that trace stellar continuum emission, the Paschen α and H α recombination lines, and the 3.3 μ m polycyclic aromatic hydrocarbon feature and its underlying continuum. With this approach, we identify 292 embedded cluster candidates for which we characterize their ages, masses, and levels of line-of-sight extinction by comparing the photometric data to predictions from stellar population models. The embedded cluster candidates have a median age of 4.5 Myr and an average line-of-sight extinction 〈 A V 〉 = 6.0 mag. We determine lower limits on source stellar masses, resulting in a median stellar mass of 10 3 M ⊙ . We use this sample of embedded cluster candidates to train multiple convolutional neural network models to carry out deep transfer learning-based searches for embedded clusters. With the aim of optimizing models for future catalog production, we compare results for four variations of training data using two neural networks. Confusion matrices for all eight model configurations, as well as inter-model identification trends, are presented. With refinement of the training sample, we determine that optimized models could serve as a pathway for future embedded cluster identification beyond our 11 galaxy sample.

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Publication Details

Journal
The Astronomical Journal
Published
2025-11-21
DOI
https://doi.org/10.3847/1538-3881/ae10ad
Citations
1
Primary Topic
Astrophysics and Star Formation Studies
Type
article
Field-Weighted Citation Impact
1.15
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article

PAH Marks the Spot: Digging for Buried Clusters in Nearby Star-forming Galaxies

Karin M. Sandstrom, David A. Thilker, G. A. Graham, M. Boquien et al.
1 citations
The Astronomical Journal
Astrophysics and Star Formation Studies
1.15
article

PAH Marks the Spot: Digging for Buried Clusters in Nearby Star-forming Galaxies

Karin M. Sandstrom, David A. Thilker, G. A. Graham, M. Boquien, Aida Wofford, Jaeyeon Kim, Emilio Mendez, Ryan Chown, Erik Rosolowsky, J. Eduardo Méndez-Delgado, Hamid Hassani, Elias K. Oakes, Ashley T. Barnes, Thomas G. Williams, S. Crowe, Tony D. Weinbeck, Kirsten L. Larson, Kathryn Grasha, Eva Schinnerer, Bradley C. Whitmore, Oleg Y. Gnedin, A. Amiri, Jessica Sutter, Adam K. Leroy, Hwihyun Kim, Rupali Chandar, Leonardo Úbeda, Stephen Hannon, Daniel A. Dale, R. Indebetouw, S. Dhileepkumar, Hannah Koziol, Qiushi Tian, M. Jimena Rodríguez, K. Conder, Janice Lee, Chase L. Smith, Kelsey Sako, Elisabeth Brann, Nicole A. Imming, Zachary Pleska
article en
1 citations

Abstract

Abstract The joint capabilities of the Hubble Space Telescope (HST) and JWST allow for an unparalleled look at the early lives of star clusters at near- and mid-infrared wavelengths. We present here a multiband analysis of embedded young stellar clusters in 11 nearby, star-forming galaxies, using the PHANGS-JWST and PHANGS-HST data sets. We use the Zooniverse citizen science platform to conduct an initial by-eye search for embedded clusters in near-UV/optical/near-infrared images that trace stellar continuum emission, the Paschen α and H α recombination lines, and the 3.3 μ m polycyclic aromatic hydrocarbon feature and its underlying continuum. With this approach, we identify 292 embedded cluster candidates for which we characterize their ages, masses, and levels of line-of-sight extinction by comparing the photometric data to predictions from stellar population models. The embedded cluster candidates have a median age of 4.5 Myr and an average line-of-sight extinction 〈 A V 〉 = 6.0 mag. We determine lower limits on source stellar masses, resulting in a median stellar mass of 10 3 M ⊙ . We use this sample of embedded cluster candidates to train multiple convolutional neural network models to carry out deep transfer learning-based searches for embedded clusters. With the aim of optimizing models for future catalog production, we compare results for four variations of training data using two neural networks. Confusion matrices for all eight model configurations, as well as inter-model identification trends, are presented. With refinement of the training sample, we determine that optimized models could serve as a pathway for future embedded cluster identification beyond our 11 galaxy sample.

The Astronomical JournalVol. 170(6)
Bryn Mawr College (US), Australian National University (AU), University of Wyoming (US), Centre National de la Recherche Scientifique (FR), Space Telescope Science Institute (US), European Southern Observatory (DE), Whitman College (US), University of Connecticut (US), Johns Hopkins University (US), Consejo Nacional de Investigaciones Científicas y Técnicas (AR), University of Alberta (CA), Cal Poly Humboldt (US), Université Côte d'Azur (FR), University of Utah (US), National Radio Astronomy Observatory (US), University of Michigan (US), Ensenada Institute of Technology (MX), University of California San Diego (US), University of Oxford (GB), Kavli Institute for Particle Astrophysics and Cosmology (US), Max Planck Institute for Astronomy (DE), Université Bourgogne Franche-Comté (FR), Gemini South Observatory (CL), Observatoire de la Côte d’Azur (FR), University of Virginia (US), The Ohio State University (US), Lycoming College (US), Rice University (US), University of Arkansas at Fayetteville (US), Universidad Nacional de La Plata (AR), Universidad Nacional Autónoma de México (MX), University of Toledo (US), California State Polytechnic University (US)
Openalex Percentile: Top 15%
Astrophysics and Star Formation Studies
1.15
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