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".

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

A DESI DR1 Search for Directional Gradients in Galaxy Population and High-Redshift Quasar Observables

Evolutionary spectrum inversion and analysis

Separating Stars from Quasars: Machine Learning Investigation Using Photometric Data

Estimating the Quasar Formation Rate Using Sloan Digital Sky Survey Data

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

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

Finding the upper bounds on neutral fraction in the IGM using Dark Gap Statistics

Analysis of far-infrared and X-ray properties of quasars using astronomical catalogues and statistical tests.

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