243 citations · 6.4k across the 138 of their papers we have counts for
12 papers · 1 filter
Bright, Relatively Isolated Star Clusters in PHANGS-HST Galaxies: Aperture Corrections, Quantitative Morphologies, and Comparison with Synthetic Stellar Population Models
Sinan Deger, Janice C. Lee, Bradley C. Whitmore +14
Using PHANGS-HST NUV-U-B-V-I imaging of 17 nearby spiral galaxies, we study samples of star clusters and stellar associations, visually selected to be bright and relatively isolate…
X-ray binaries in M51 I: catalog and statistics
Jared R. Rice, Blagoy Rangelov, Andrea Prestwich +3
We used archival data from the \emph{Chandra X-ray Observatory} (\emph{Chandra}) and the \emph{Hubble Space Telescope}, to identify 334 candidate X-ray binary (XRB) systems and the…
Star Cluster Classification in the PHANGS-HST Survey: Comparison between Human and Machine Learning Approaches
Bradley C. Whitmore, Janice C. Lee, Rupali Chandar +21
When completed, the PHANGS-HST project will provide a census of roughly 50,000 compact star clusters and associations, as well as human morphological classifications for roughly 20…
The Star Formation History of a Post-Starburst Galaxy Determined From Its Cluster Population
Rupali Chandar, Angus Mok, K. Decker French +2
The recent star formation histories (SFHs) of post-starburst galaxies have been determined almost exclusively from detailed modeling of their composite star light. This has provide…
PHANGS-HST: New Methods for Star Cluster Identification in Nearby Galaxies
David A. Thilker, Bradley C. Whitmore, Janice C. Lee +14
We present an innovative and widely applicable approach for the detection and classification of stellar clusters, developed for the PHANGS-HST Treasury Program, an -to- ban…
Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine learning
Benne W. Holwerda, John F. Wu, William C. Keel +10
Wu & Peek (2020) predict SDSS-quality spectra based on Pan-STARRS broad-band \textit{grizy} images using machine learning (ML). In this letter, we test their prediction for a uniqu…