274 citations · 1.5k across the 43 of their papers we have counts for
3 papers · 1 filter
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…
Retrieving Internal Kinematics of Galaxies with Deep Learning using Single-Band Optical Images
Sakina Hansen, Christopher J. Conselice, Amelia Fraser-McKelvie +1
Using deep machine learning we show that the internal velocities of galaxies can be retrieved from optical images trained using 4596 systems observed with the SDSS-MaNGA survey. Us…
Galaxy Merger Rates up to z 3 using a Bayesian Deep Learning Model A Major-Merger classifier using IllustrisTNG Simulation data
Leonardo Ferreira, Christopher J. Conselice, Kenneth Duncan +3
Merging is potentially the dominate process in galaxy formation, yet there is still debate about its history over cosmic time. To address this we classify major mergers and measure…