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We try to find an optimized methodology to constrain the cosmological parameters using the quasar dataset. Quasars can be the potential cosmic probe that can fill up the gap between the farthest observed Type Ia Supernovae and the Cosmic Microwave Background CMB. Quasars can be observed to the highest redshift of z ≈ 7.1. It can give valuable insight into the tensions of the cosmological parameters. Use of the quasar dataset on the cosmological parameter constraints is done by the use of an empirical non-linear equation of the luminosity of UV and the luminosity of X-ray of quasar. We use different statistical tools to validate this empirical relation. We observe that the strong correlation of the quasar parameter is independent of the cosmology. We introduce a methodology to break this strong correlation between the quasar parameter and the way to explore the model parameter space effectively.  We also develop a machine learning algorithm that can be used to reconstruct the functional form of an observable quantity in a model-independent, non-parametric way. We use this algorithm in the scattered dataset of quasars to get the Cosmological inferences and to check the validity of the empirical equation that connects the luminosity of X-ray and UV of quasars. We have completed our analysis for the low-redshift quasars, and we are now planning to use the same set of algorithms on other quasar data and on different quasar datasets.

BIO

Current position: Post-doc researcher at the Korea Astronomy and Space Science Institute, Daejeon, South Korea

PhD institute: Indian Institute of Science Education and Research, Mohali, India

Research areas: Quasar physics, Applications of Quasar data on Cosmology, Development of statistical and machine learning techniques on Cosmology.

Host: Yi Mao