64 citations · 93 across the 6 of their papers we have counts for
3 papers · 1 filter
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…