Using machine learning to predict the mass of quasar supermassive black holes

A tutorial on classification and photometric redshift regression of astronomical sources using supervised machine learning techniques.

A repository containing our code for our paper, "Photometric identification of compact galaxies, stars and quasars using multiple neural networks".

Evolutionary spectrum inversion and analysis

Code used in Stone et al. (in review, ApJ) for GAMA quasar host star formation history paper analysis.

Separating Stars from Quasars: Machine Learning Investigation Using Photometric Data

Python code to attempt to identify high-redshift quasars using their infrared colours. The program uses a decision tree classifier to learn the quasar and dwarf star distributions in the selected colour spaces, and a subset of new data is used to trial run the code.

Demonstration of comprehensive machine learning analysis in iPython of the quasar candidates catalog by Richards et al., ApJS 219 (2015).

Estimating the Quasar Formation Rate Using Sloan Digital Sky Survey Data

Analysis of the SINFONI integral-field data for powerful radio-quasar 3C 297

Github repository for machine learning application on quasar selection and the discrimination between stars and galaxies.