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Sultan Hassan

Sultan Hassan
Tombaugh Postdoctoral Fellow
Research Staff

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Expertise: Bayesian Inference and Machine Learning



Biographical Info

I use large-scale semi-numerical simulations, with insights from high resolution radiative transfer simulations, to study how reionization began, evolved and completed. I am interested to understand how changes in the small sub-kpc scales impact the large cosmological scales, particularly how changes in ionizing source populations might change the large scale 21cm fluctuations. I am currently focused on exploring the origin of Damped Lyman Alpha kinematics in relation to their hosting galaxies and halos properties as a function of cosmic time. I also employ MCMC and Machine Learning techniques to extract the astrophysical and cosmological information from the ongoing and upcoming radio interferometer experiments such the SKA, HERA and LOFAR. Recently, I have designed a convolutional neural network to identify reionization sources (AGN vs Galaxies) from large scale 21cm-images, a tool is being prepared for use in future 21cm surveys.