# Research

Current research:

• Denoising galaxy images using deep learning techniques – testing the importance of incorporating image Point Spread Function in the network in various ways
•  Stellar mass calculation of galaxy clusters from the Planck all sky survey
•  Supernova and variable star detection and analysis using active anomaly detection
•  Detection and morphological classification of dwarf galaxies from the MATLAS
survey
•  Detection and classification of tidal remnants around massive galaxies from the
MATLAS survey

Past research :

• Analysing the effect of including measurement errors in machine learning and how it improves astronomical applications using Probabilistic Random Forests.
• Supernova photometric classification using active learning.
• Comparison of machine learning techniques to classify galaxies according to their morphological types.
•  Formulating a pre-processing technique for astronomical software such as Source Extractor using anisotropic diffusion filtering.
•  Investigating merger remnants around and within early-type galaxies.
•  Detection and analysis of dwarf galaxy environments from the MATLAS survey.