2 papers
astro-ph.GA2022
A Simulation Driven Deep Learning Approach for Separating Mergers and Star Forming Galaxies: The Formation Histories of Clumpy Galaxies in all the CANDELS Fields
Leonardo Ferreira, Christopher J. Conselice, Ulrike Kuchner +1
Being able to distinguish between galaxies that have recently undergone major merger events, or are experiencing intense star formation, is crucial for making progress in our under…
astro-ph.GA2020
Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning
C. Tohill, L. Ferreira, C. J. Conselice +2
At high redshift, due to both observational limitations and the variety of galaxy morphologies in the early universe, measuring galaxy structure can be challenging. Non-parametric…