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Machine Learning for Galaxy Classification
My research focuses on applying machine learning to the classification and characterisation of galaxies using photometric data. I work on both morphological and activity based classification, exploring how information from large astronomical surveys can be used to identify galaxy properties without relying exclusively on spectroscopy.
A key aim of this work is to develop scalable methods that can be applied to the large and heterogeneous datasets produced by current and future sky surveys.
SpecLess Python Package
SpecLess Python Package
SpecLess is a Python package developed for photometry based galaxy classification using machine learning. It provides tools for morphological and activity based classification and is designed for scalable applications across large astronomical surveys.
Image Credit: ESO
Ionized Gas and the Interstellar Medium in M33
Ionized Gas and the Interstellar Medium in M33
I also contribute to an ongoing international research project investigating the ionized gas structure and interstellar medium of the M33 Galaxy. In this work, I use MUSE observations to study the physical properties of ionized gas and to support analyses of the galaxy’s gaseous environment. My contribution includes research and analytical work aimed at understanding the spatially resolved structure of the interstellar medium in M33.
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