From the 1 of 5 linked papers with an AI index.
5 papers
Star-forming clump detection in nearby galaxies using Faster R-CNN and imaging data from CLAUDS and HSC-SSP
Jürgen J. Popp, Hugh Dickinson, Stephen Serjeant +4
The paper presents a deep‑learning object detection pipeline based on Faster R‑CNN and a Zoobot backbone to locate giant star‑forming clumps in low‑redshift galaxies using six‑band…
Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey
James Pearson, Hugh Dickinson, Stephen Serjeant +22
We present morphological classifications of over 41,000 galaxies out to across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty…
Galaxy Zoo Evo: 1 million human-annotated images of galaxies
Mike Walmsley, Steven Bamford, Hugh Dickinson +17
We introduce Galaxy Zoo Evo, a labeled dataset for building and evaluating foundation models on images of galaxies. GZ Evo includes 104M crowdsourced labels for 823k images from fo…
The La Silla Schmidt Southern Survey
Adam A. Miller, Natasha S. Abrams, Greg Aldering +75
We present the La Silla Schmidt Southern Survey (LS4), a new wide-field, time-domain survey to be conducted with the 1 m ESO Schmidt telescope. The 268 megapixel LS4 camera mosaics…
Improving Deep Ensembles by Estimating Confusion Matrices
Danil Kuzin, Olga Isupova, Steven Reece +1
Ensembling in deep learning improves accuracy and calibration over single networks. The traditional aggregation approach, ensemble averaging, treats all individual networks equally…