Abstract Details
| Name: Ranbir Sharma Affiliation: IISER, Mohali Conference ID: ASI2019_96 Title : Dark energy equation of state reconstruction by Principal Component Analysis Authors and Co-Authors : Ranbir Sharma, IISER Mohali, Punjab Dr. Ankan Mukherjee, IISER Mohali, Punjab Dr. H K Jassal, IISER Mohali, Punjab Abstract Type : Oral Abstract Category : General Relativity and Cosmology Abstract : Principal Component Analysis (PCA) is an eigenvector-based multivariate analysis technique, used to find trends and features from a particular data-set. It has been applied to a vast variety of problems, in many diverse fields, from image processing to reconstruction of cosmological quantities. We use the PCA technique to the reconstruction of Hubble parameter, distance modulus and equation of state parameter (EoS) of dark energy. Dark energy is that negative pressure which drives the acceleration of the universe, and EoS dictates its effect on the dynamics of the Universe. We show that a combination of correlation tests and the PCA algorithm can be applied as a powerful reconstruction tool and also can be used to find out a (semi)analytical form of the underlying curve of a given data-set without any prior biases. For this analysis, we consider Supernova type Ia and Hubble parameter data. We carry out reconstruction of EoS of dark energy with two different approaches: direct reconstruction of the dark energy EoS, and, reconstruction of this by reconstruction of the Hubble parameter and distance modulus as a function of redshift using the Hubble parameter data and distance modulus of supernovae of type Ia. We test both these approaches with the simulated lcdm data-set. We show that the data allows only small deviations from the lcdm model at low redshifts. |