Kieran Cleary

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About

Kieran Cleary is a researcher at the forefront of astronomical instrumentation and data analysis, with a particular focus on polarimetric imaging and the mitigation of observational artefacts. His work addresses a critical challenge in astrophysics: the contamination of sensitive polarization measurements by unwanted image features such as internal telescope reflections or satellite trails. Cleary’s most notable contribution, the 2019 paper "Eliminating artefacts in polarimetric images using deep learning," introduces a novel machine-learning approach to automatically detect and remove these contaminants, significantly improving the reliability of data from instruments like the Robotic Polarimeter. Although this seminal work has garnered 5 citations to date, its impact lies in its practical application for real-time data cleaning, a growing necessity as satellite constellations increasingly interfere with ground-based observations. Cleary’s research bridges the gap between traditional observational techniques and modern computational methods, offering a scalable solution for high-precision polarimetry. His efforts are particularly valuable for students and researchers working on imaging polarimeters, where artefact-free data is essential for accurate studies of magnetic fields, interstellar dust, and exoplanetary atmospheres.