Abstract
We present an algorithm based on neural networks that predicts the mass ratio in a binary black hole collision out of given Gravitational Wave (GW) signals. In this brief analysis, the network is trained with a small sample of GW signals generated with numerical simulations. The effectiveness of the algorithm is evaluated with GW generated again with simulations with given mass ratios unknown to the network and found to be in the worst scenarios less than 10%.
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