1 citations · 1 across the 2 of their papers we have counts for
5 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…
Finding radio transients with anomaly detection and active learning based on volunteer classifications
Alex Andersson, Chris Lintott, Rob Fender +9
In this work we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide…
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner
Automating anomaly detection is an open problem in many scientific fields, particularly in time-domain astronomy, where modern telescopes generate millions of alerts per night. Cur…
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…