activity
20202024
most citedGalaxy Merger Rates up to z 3 using a Bayesian Deep Learning Model A Major-Merger classifier using IllustrisTNG Simulation data

90 citations · 116 across the 6 of their papers we have counts for

collaborators

8 papers

astro-ph.GA20226 cited

The JWST Hubble Sequence: The Rest-Frame Optical Evolution of Galaxy Structure at

Leonardo Ferreira, Christopher J. Conselice, Elizaveta Sazonova +13

We present results on the morphological and structural evolution of a total of 4265 galaxies observed with JWST at in the JWST CEERS observations that overlap with th…

astro-ph.GA202211 cited

Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks

Ting-Yun Cheng, H. Domínguez Sánchez, J. Vega-Ferrero +54

We compare the two largest galaxy morphology catalogues, which separate early and late type galaxies at intermediate redshift. The two catalogues were built by applying supervised…

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.GA20219 cited

Emission Line Galaxies in the SHARDS Frontier Fields I: Candidate Selection and the Discovery of Bursty Hα Emitters

Alex Griffiths, Christopher J. Conselice, Leonardo Ferreira +7

Emission line galaxies provide a crucial tool for the study of galaxy formation and evolution, providing a means to trace a galaxy's star formation history or metal enrichment, and…

astro-ph.GA2021

Galaxy Evolution in all Five CANDELS Fields and IllustrisTNG: Morphological, Structural, and the Major Merger Evolution to

A. Whitney, L. Ferreira, C. J. Conselice +1

A fundamental feature of galaxies is their structure, yet we are just now understanding the evolution of structural properties in quantitative ways. As such, we explore the quantit…

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…