Star Cluster Formation and Evolution in M101: An Investigation with the Legacy Extragalactic UV Survey
S. T. Linden, G. Perez, D. Calzetti, S. Maji, M. Messa, B. C., Whitmore, R. Chandar, A. Adamo, K. Grasha, D. O. Cook, B. G. Elmegreen, D. A., Dale, E. Sacchi, E. Sabbi, E. K. Grebel, L. Smith

TL;DR
This study uses Hubble Space Telescope data and machine learning to catalog and analyze star clusters in galaxy M101, revealing environment-dependent disruption rates and demonstrating high classification accuracy.
Contribution
It provides the most complete catalog of star clusters in M101 and evaluates the effectiveness of a neural network classifier compared to human experts.
Findings
StarcNet classifies star clusters with 80-90% accuracy.
Cluster disruption rates vary with environment in M101.
Disruption trends are weaker than in other spiral galaxies.
Abstract
We present Hubble Space Telescope WFC3/UVIS (F275W, F336W) and ACS/WFC optical (F435W, F555W, and F814W) observations of the nearby grand-design spiral galaxy M101 as part of the Legacy Extragalactic UV Survey (LEGUS). Compact sources detected in at least four bands were classified by both human experts and the convolutional neural network StarcNet. Human experts classified the 2,351 brightest sources, retrieving star clusters. StarcNet, trained on LEGUS data not including M101, classified all 4,725 sources detected in four bands, retrieving star clusters. The combined catalog represents the most complete census to date of compact star clusters in M101. We find that for the 2,351 sources with both a visual- and ML-classification StarcNet is able to reproduce the human classifications at high levels of accuracy (), which is equivalent to the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
