A Deep Learning Approach for Characterizing Major Galaxy Mergers

no code implementations • 9 Feb 2021 • Skanda Koppula, Victor Bapst, Marc Huertas-Company, Sam Blackwell, Agnieszka Grabska-Barwinska, Sander Dieleman, Andrea Huber, Natasha Antropova, Mikolaj Binkowski, Hannah Openshaw, Adria Recasens, Fernando Caro, Avishai Deke, Yohan Dubois, Jesus Vega Ferrero, David C. Koo, Joel R. Primack, Trevor Back

Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation.

regression

Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning

no code implementations • 24 Sep 2020 • Ting-Yun Cheng, Marc Huertas-Company, Christopher J. Conselice, Alfonso Aragón-Salamanca, Brant E. Robertson, Nesar Ramachandra

Based on this, the main result in this work is how well our unsupervised method matches visual classifications and physical properties, as well as providing an independent classification that is more physically meaningful than any visually based ones.

Astrophysics of Galaxies