30 citations · 91 across the 9 of their papers we have counts for
6 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…
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 for Astronomical Transients
Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner
Automating real-time anomaly detection is essential for identifying rare transients, with modern survey telescopes generating tens of thousands of alerts per night, and future tele…
Enabling Unsupervised Discovery in Astronomical Images through Self-Supervised Representations
Koketso Mohale, Michelle Lochner
Unsupervised learning, a branch of machine learning that can operate on unlabelled data, has proven to be a powerful tool for data exploration and discovery in astronomy. As large…
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…