activity
20202024
most citedQuantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning

34 citations · 64 across the 6 of their papers we have counts for

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Showing astro-ph.GAShow all

6 papers · 1 filter

astro-ph.GA2024★ 1 cited

Improved constraints on Galactic Centre ejection of hypervelocity stars based on novel search method

Sill Verberne, Elena Maria Rossi, Sergey E. Koposov +6

Hypervelocity stars (HVSs) are stars which have been ejected from the Galactic Centre (GC) at velocities of up to a few thousand km/s. They are tracers of the Galactic potential an…

astro-ph.GA2023★ 22 cited

A Robust Study of High-Redshift Galaxies: Unsupervised Machine Learning for Characterising morphology with JWST up to z ~ 8

Clár-Bríd Tohill, Steven Bamford, Christopher Conselice +4

Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical intera…

astro-ph.GA2023★ 1 cited

Exploring the Morphologies of High Redshift Galaxies with Machine Learning

Clár-Bríd Tohill, Steven Bamford, Christopher Conselice

The morphology of a galaxy has been shown to encode the evolutionary history and correlates strongly with physical properties such as stellar mass, star formation rates and past me…

astro-ph.GA2022★ 6 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.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★ 34 cited

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…