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