Notes on Artificial Intelligence, Machine Learning, Deep Neural Networks & Big Data

using information theory for black hole discovery

We recently published a paper in the journal Nature about an "An intermediate-mass black hole in the centre of the globular cluster 47 Tucanae". For the first time, we are effectively combining information obtained from N-body simulations, pulsar observations and use information theory to probe whether there is a black hole in centers of globular clusters.

Click below for our paper: 

Kiziltan Nature

applications to astrophysical problems

  • Come and listen to my talk at the upcoming "Detecting the Unexpected: Discovery in the Era of Astronomically Big Data" conference on "Pushing the Frontiers of Astronomical Discovery with Deep Learning" to be held in Baltimore, STScI, between 27 February-2 March 2017.

applied statistics

  • asymmetric error bars: the asymmetric nature of error bars are often ignored, and the errors are typically approximated with Gaussian models. Take a look at our paper on the "Neutron Star Mass Distribution". We developed a novel approach which is generically applicable for data with asymmetric error bars. For technical detail see the Appendix, subsection on Model Formulation. Don't ignore the value of information that may be 'hidden' in the errors!

data science

tools, articles, and tutorials I find useful

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