SMuK 2023 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

AKPIK: Arbeitskreis Physik, moderne Informationstechnologie und Künstliche Intelligenz

AKPIK 4: Neural Networks II

AKPIK 4.5: Talk

Wednesday, March 22, 2023, 16:45–17:00, ZEU/0118

Binary Black Hole Parameter Estimation using Deep Neural Networks — •Markus Bachlechner, David Bertram, Philipp Otto, Oliver Pooth, and Achim Stahl — III. Physikalisches Institut B, RWTH Aachen

As the first of the third-generation of gravitational wave detectors, the proposed Einstein Telescope is expected to be at least an order of magnitude more sensitive compared to current interferometers like LIGO and Virgo. On the one hand, the higher sensitivity increases the observable volume. On the other hand, high sensitivity in the low-frequency band leads to significantly earlier detection and observation for some coalescences like binary neutron stars. These early observations make it possible to send multi-messenger alerts before the merger. Applying a fast analysis handling event detection, classification, and estimation in real time is essential. This talk presents an approach for parameter estimation of binary black holes using deep neural networks.