In the past few years I have been very keen to determine how machine learning can help us answer astronomical problems. Below are some examples of papers from my group which have addressed basic questions about galaxy formation and evolution with deep learning. As part of my machine learning work I am a Alan Turing Fellow at the Alan Turing Institute, London.
Some recent papers
Cheng, T.-Y., Conselice, C. and 58 colleagues 2021. “Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks.” Monthly Notices of the Royal Astronomical Society 507, 4425–4444. doi:10.1093/mnras/stab2142
Tohill, C., Ferreira, L., Conselice, C.J., Bamford, S.P., Ferrari, F. 2021. “Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning.": The Astrophysical Journal 916. doi:10.3847/1538-4357/ac033c
Cheng, T.-Y., Huertas-Company, M., Conselice, C.J., Aragon-Salamanca, A., Robertson, B.E., Ramachandra, N. 2021. “Beyond the hubble sequence - exploring galaxy morphology with unsupervised machine learning.” Monthly Notices of the Royal Astronomical Society 503, 4446–4465. doi:10.1093/mnras/stab734
Ferreira, L., Conselice, C.J. et al. 2020. “Galaxy Merger Rates up to z~3 Using a Bayesian Deep Learning Model: A Major-merger Classifier Using IllustrisTNG Simulation Data.” The Astrophysical Journal 895. doi:10.3847/1538-4357/ab8f9b
Cheng, T.-Y., Li, N., Conselice, C.J., Aragon-Salamanca, A., Dye, S., Metcalf, R.B. 2020. “Identifying strong lenses with unsupervised machine learning using convolutional autoencoder”, Monthly Notices of the Royal Astronomical Society 494, 3750–3765. doi:10.1093/mnras/staa1015