Dark matter, making 80% of the known universe is the most elusive form of matter because of its non-interacting nature. Detecting dark matter is an open and difficult problem however, its existence can be inferred from the effect of its gravitational field on the background stars/galaxies. I targeted to workout the problem utilizing the concept of gravitational lensing; evaluating the distorted ellipticity of the galaxies give useful insights of the dark matter position in the sky.

Inspired from the literature, I utilized the bayesian inference approach to estimate the dark matter position in galactic images taken by NASA. Using the bayes rule, I calculated the posterior distribution from the approximated model of observing galaxies ellipticities given the dark matter position and uniformly distributed prior. This simple, but effective methodology worked outstandingly to predict the dark matter positions in dark sky, unriddling one of the most challenging problems in astronomy.