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T: Fachverband Teilchenphysik

T 96: Data, AI, Computing 7 (uncertainties, likelihoods)

T 96.5: Vortrag

Donnerstag, 7. März 2024, 17:00–17:15, Geb. 30.33: MTI

Binary Black Hole Parameter Estimation using a Conditioned Normalizing Flow — •Markus Bachlechner, Oliver Pooth, and Achim Stahl — III. Physikalisches Institut B, RWTH Aachen University

The proposed Einstein Telescope is the first of the third-generation gravitational wave detectors. It is expected to reach a noise level at least an order of magnitude lower than current interferometers like LIGO and Virgo. The thus improved sensitivity increases the observable volume and extends the time window in which the inspiral phase of binary systems is measurable. To analyze the resulting vast amounts of data efficiently, Neural Networks (NNs) can be utilized. This talk presents a fast Binary Black Hole parameter reconstruction by applying a conventional convolutional NN which conditions a subsequent Normalizing Flow (NF). Using the NF, an approximated posterior parameter distribution on an event-by-event basis is obtained, and thus uncertainties can be estimated.