Classification of galaxies is traditionally associated with their morphologies through visual
inspection of images. The amount of data to come render this task, inhuman and Machine …

Context. One of the most important properties of a galaxy is the total stellar mass, or
equivalently the stellar mass-to-light ratio (M/L). It is not directly observable, but can be …

Machine learning techniques have proven to be increasingly useful in astronomical
applications over the last few years, for example in object classification, estimating redshifts …

We apply four statistical learning methods to a sample of 7941 galaxies (z< 0.06) from the
Galaxy And Mass Assembly survey to test the feasibility of using automated algorithms to …

We present our results from training and evaluating a convolutional neural network (CNN) to
predict galaxy shapes from wide-field survey images of the first data release of the Dark …

The morphological diversity of galaxies is a relevant probe of galaxy evolution and
cosmological structure formation. However, in large sky surveys, even the morphological …

Galaxy morphology characterisation is an important area of study, as the type and formation
of galaxies offer insights into the origin and evolution of the universe. Owing to the increased …

We present a deep learning model to predict the r-band bulge-to-total luminosity ratio (B/T)
of nearby galaxies using their multiband JPEG images alone. Our Convolutional Neural …

We study the application of machine learning techniques for the detection of the astrometric
signature of dark matter substructure. In this proof of principle, a population of dark matter …

Machine learning is becoming a popular tool to quantify galaxy morphologies and identify
mergers. However, this technique relies on using an appropriate set of training data to be …