64 citations · 84 across the 8 of their papers we have counts for
8 papers
Euclid: Quick Data Release (Q1) -- Exploring the detailed visual morphology of galaxies in clusters
P. Mas-Buitrago, S. Kruk, D. O'Ryan +167
Galaxy clusters provide unique laboratories for studying environmental effects on galaxy evolution. The morphology--density (--) and morphology--cluster-centric radius (--…
Deep Learning Segmentation of Spiral Arms and Bars for 600,000 Galaxies in DESI
Ashley Spindler, Mike Walmsley, Karen L. Masters +4
We present a catalogue of segmentation maps identifying the extent of spiral arms and bars of 639,636 galaxies in the DESI Legacy Survey. To produce these maps, we have trained a d…
Galaxy Zoo CEERS: Bar fractions up to z~4.0
Tobias Géron, R. J. Smethurst, Hugh Dickinson +22
We study the evolution of the bar fraction in disc galaxies between using multi-band coloured images from JWST CEERS. These images were classified by citizen scient…
Euclid Quick Data Release (Q1), A first look at the fraction of bars in massive galaxies at
Euclid Collaboration, M. Huertas-Company, M. Walmsley +336
Stellar bars are key structures in disc galaxies, driving angular momentum redistribution and influencing processes such as bulge growth and star formation. Quantifying the bar fra…
Euclid Quick Data Release (Q1): First visual morphology catalogue
Euclid Collaboration, M. Walmsley, M. Huertas-Company +352
We present a detailed visual morphology catalogue for Euclid's Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the…
COSMOS-Web: The emergence of the Hubble Sequence
M. Huertas-Company, M. Shuntov, Y. Dong +29
Leveraging the wide area coverage of the COSMOS-Web survey, we quantify the abundance of different morphological types from with unprecedented statistics and establish ro…
Scaling Laws for Galaxy Images
Mike Walmsley, Micah Bowles, Anna M. M. Scaife +17
We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annota…
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
Mike Walmsley, Anna M. M. Scaife, Chris Lintott +9
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to an…