Research

My core research revolves around using statistics and machine learning to leverage massive astronomical surveys for scientific discovery. In Lochner & Bassett (2021), we designed a framework called Astronomaly which uses machine learning to discover anomalous objects in large astronomical datasets. The figure on the left shows a frontend designed to extract information from an expert user which, when combined with machine learning, provides personalised anomaly detection for scientific discovery.

The figure on the right is from Lochner & Bassett (2021) showing an example of anomalous galaxies in the public dataset Galaxy Zoo. These are examples of merging and interacting galaxies, extracted from a dataset of over 60 000 sources, most of which are not as interesting. The image below is from Lochner et al. (2023) showing an example anomalous radio galaxy detected in MeerKAT data using Astronomaly. This source is called "SAURON" - a steep and uneven ring of non-thermal radiation, and while it is not yet clear what physics can produce something like this, it could be the remnant of a merger of two supermassive black hole.

Another area of my research involves the Vera C. Rubin Observatory, under construction in Chile. The Rubin Observatory will undertake an ambitious 10-year survey of the sky, called the Legacy Survey of Space and Time (LSST), which is expected to revolutionise multiple areas of astronomy - from asteroid discovery to understanding the most distant universe. The exact procedure to take observations during this survey, called the observing strategy, is still not yet decided. In Lochner et al. (2022) I led an international team of over 30 scientists to evaluate the impact of these choices on cosmology with LSST. The figure on the right shows changes in the DETF Figure of Merit, a measure of how well we can study dark energy, as a function of observing strategy properties.

Visit my publications page for a list of publications and the student supervision page to meet my research group.

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