Astronomical data sets have been growing at an almost exponential rate, especially with the upcoming SKA, LSST, JWST, and so on, which requires the adoption of new, automated techniques for data reduction and analysis. Machine-learning methods have recently gained popularity in Astrophysics and Cosmology and may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. For the astronomers at UWC, we are working on applying machine-learning techniques to the science exploitation of radio data from MeerKAT and upcoming SKA.
Recent Publications
- “Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning”, Rafieferantsoa, M., Andrianomena, S., & Davé, R. 2018,MNRAS, 479, 4509
- “The FIRST Classifier: compact and extended radio galaxy classification using deep Convolutional Neural Networks”, Alhassan, W., Taylor, A.~R., & Vaccari, M. 2018, MNRAS, 480, 2085
- “Multi-wavelength Properties of Radio- and Machine-learning-identified Counterparts to Submillimeter Sources in S2COSMOS”，An, Fang Xia; Simpson, J. M.; Smail, Ian, et al. 2019, APJ, 886, 48
- “Classifying galaxies according to their HI content”, Andrianomena, S., Rafieferantsoa, M., & Davé, R. 2020, MNRAS, 492, 5743
- “Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA”, Hassan, S., Andrianomena, S., & Doughty, C. 2019, arXiv:1907.07787
People
- Mattia Vaccari
- Sambatra Andrianomena
- Sultan Hassan
- Mika Rafieferantsoa
- Chaka Mofokeng
- Palesa Nombula
- Sibusiso Mdhluli
- Fangxia An
- Siyambonga Matshawule
- Tumelo Mangena