People

Earl Bellinger is an astrophysicist mainly working on asteroseismology and stellar evolution. His research combines theoretical stellar models with large observational data sets in order to further our understanding of stars, planets, galaxies, and black holes.

What do you do with Data Science?

My research makes extensive use of machine learning in order to bridge the gap between theory and observation in astrophysics. In particular, I generate theoretical stellar models and use them as training data for regression and classification tasks applied to astronomical observations. Some applications include estimating stellar ages, measuring distances to external galaxies, and determining the stability of multiple-star systems. Some of my publications include: *Vynatheya, P., Hamers, A. S., Mardling, R. A., Bellinger, E. P. (2022). Algebraic and machine learning approach to hierarchical triple-star stability. Monthly Notices of the Royal Astronomical Society, 516 (3). Bellinger, E. P. (2020). A seismic scaling relation for stellar age II. The red giant branch. MNRAS Letters, 492 (1), doi:10.1093/mnrasl/slz178. Bellinger, E. P., Kanbur, S. M., Bhardwaj, A., Marconi, M. (2020). When a Period Is Not a Full Stop: Light Curve Structure Reveals Fundamental Parameters of Cepheid and RR Lyrae Stars. Monthly Notices of the Royal Astronomical Society, 491 (4). *Hon, M., Bellinger, E. P., Hekker, S., Stello, D., Kuszlewicz, J. S. (2020). Asteroseismic Inference of Subgiant Evolutionary Parameters with Deep Learning, Monthly Notices of the Royal Astronomical Society. Bellinger, E. P., Angelou, G. C., Hekker, S., Basu, S., Ball, W., Guggenberger, E. (2016). Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning. The Astrophysical Journal. (*indicates student-led publication)

Edit profile