Abstract Details
| Name: Sarvesh Kumar Yadav Affiliation: Raman Research Institute, Bangalore Conference ID: ASI2024_142 Title : A machine learning based method to detect primordial gravitational wave signature Authors and Co-Authors : Sarvesh Kumar Yadav, Rajib Saha, Tarun Souradeep Abstract Type : Poster Abstract Category : Galaxies and Cosmology Abstract : Observations of the Cosmic Microwave Background (CMB) radiation have made significant contributions to our understanding of cosmology. While temperature observations of the CMB have greatly advanced our knowledge, the next frontier lies in detecting the elusive B-modes and obtaining precise reconstructions of the polarized CMB signal in general. In anticipation of proposed and upcoming CMB polarization missions, this study introduces a novel method to detect the primordial B-modes. We have developed a Bayesian Neural Network (BNN)-based approach to enhance the performance of the Internal Linear Combination (ILC) technique. Our method is applied separately to the frequency channels of both the LiteBird and ECHO (also known as CMB-Bharat) missions and its performance is rigorously assessed for both missions. Our findings demonstrate the method's efficiency in achieving precise reconstructions of CMB B-mode angular power spectrum, with errors constrained primarily by cosmic variance. |