All Outputs (3)

Predicting the ages of galaxies with an artificial neural network (2024)
Journal Article
Hunt, L. J., Pimbblet, K. A., & Benoit, D. M. (2024). Predicting the ages of galaxies with an artificial neural network. Monthly notices of the Royal Astronomical Society, 529(1), 479-498. https://doi.org/10.1093/mnras/stae479

We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of galaxies by... Read More about Predicting the ages of galaxies with an artificial neural network.

Noise reduction in single-shot images using an auto-encoder (2023)
Journal Article
Bartlett, O. J., Benoit, D. M., Pimbblet, K. A., Simmons, B., & Hunt, L. (2023). Noise reduction in single-shot images using an auto-encoder. Monthly notices of the Royal Astronomical Society, 521(4), 6318-6329. https://doi.org/10.1093/mnras/stad665

We present an application of auto-encoders to the problem of noise reduction in single-shot astronomical images and explore its suitability for upcoming large-scale surveys. Auto-encoders are a machine learning model that summarizes an input to ident... Read More about Noise reduction in single-shot images using an auto-encoder.

Quantitative morphology of galaxy clusters at X-ray wavelengths (2021)
Thesis
Hunt, L. (2021). Quantitative morphology of galaxy clusters at X-ray wavelengths. (Thesis). University of Hull. Retrieved from https://hull-repository.worktribe.com/output/4224267

We apply quantitative morphology techniques to X-ray imaging of clusters of galaxies to determine correlation between calculated parameters (CAS, Gini and M20) and features such as bow shocks, cold fronts, gas sloshing, Kelvin-Helmholtz instabilities... Read More about Quantitative morphology of galaxy clusters at X-ray wavelengths.