20-23 September 2021
Europe/Athens timezone

Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling

Not scheduled
Oral presentation


Mrs Paraskevi Nousi (Aristotle University of Thessaloniki)


We investigate the use of neural networks for surrogate modeling of non-spinning EOB BBH waveforms. Specifically, we use autoencoders to first uncover any underlying structure in the empirical interpolation coefficients and discover a spiral pattern wherein the spiral angle is linearly related to the mass ratio q of the waveforms. We then design a neural spiral module with learnable parameters, which can be added to any fully connected neural network and "informs" the network about the nature of the fitting problem, i.e., about how q is related to the coefficients via a spiral. The proposed spiral module leads to better regression errors as well as to a better mismatch between the surrogate and ground-truth waveforms, compared to baseline models without the addition of this spiral. We finally present a surrogate model for EOBNRv2 waveforms with q ranging from 1 to 8, which can generate millions of coefficients in less than a millisecond on a desktop GPU with median mismatches as low as $10^{-8}$.

Primary author

Mrs Paraskevi Nousi (Aristotle University of Thessaloniki)

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