| A new era in observational astrophysics has been inaugurated by the discovery of gravitational waves, offering a unique lens into celestial phenomena that are elusive to conventional electromagnetic detection. The potential of extracting vital parameters from gravitational waves emitted by Binary Black Hole systems is explored, leveraging the capabilities of Deep Learning Methods. A Convolutional Neural Network architecture is introduced in this work, specifically designed for the precise estimation of the masses and distances of Binary Black Hole systems. The effectiveness and robustness of the proposed architecture in accurately estimating these parameters is demonstrated. This research signifies a significant stride towards enhancing our understanding of Binary Black Hole phenomena and underscores the transformative role of Artificial Intelligence in observational astrophysics. |
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