64 citations · 93 across the 6 of their papers we have counts for
6 papers
The promise of self-supervised and active learning for Strong Lens discovery: Astronomaly applied to KiDS
Margherita Grespan, Aprajita Verma, Michelle Lochner +3
Strong gravitational lenses (SGLs) are rare systems whose discovery currently relies primarily on supervised machine learning methods trained on large simulated datasets. We presen…
A targeted machine learning approach for detecting diffuse radio emission with Astronomaly: Protege
Verlon Etsebeth, Michelle Lochner, Konstantinos Kolokythas +2
Diffuse radio emission in galaxy clusters, such as radio halos, relics, and mini halos, is a key tracer of non-thermal processes, turbulence, and magnetic fields within the intra-c…
A Guided Unconditional Diffusion Model to Synthesize and Inpaint Radio Galaxies from FIRST, MGCLS and Radio Zoo
Rémi Poitevineau, Emma Tolley, Verlon Etsebeth
We present a masked-guided approach for a denoising diffusion probabilistic model (DDPM) trained to generate and inpaint realistic radio galaxy images. The inpainting capability is…
TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS
Margherita Grespan, Hareesh Thuruthipilly, Agnieszka Pollo +3
We apply a state-of-the-art transformer algorithm to 221 deg of the Kilo Degree Survey (KiDS) to search for new strong gravitational lenses (SGL). We test four transformer enco…
Astronomaly at scale: searching for anomalies amongst 4 million galaxies
Verlon Etsebeth, Michelle Lochner, Mike Walmsley +1
Modern astronomical surveys are producing datasets of unprecedented size and richness, increasing the potential for high-impact scientific discovery. This possibility, coupled with…
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…