Gravitational wave surrogates through automated machine learning

Barsotti, Damián; Cerino, Franco; Tiglio, Manuel; Villanueva, Aarón

doi:10.48550/arxiv.2110.08901

Cited by 1 publication

(1 citation statement)

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“…the third step described above) can be very costly at high dimensions, in which case probabilistic methods could be employed, see e.g. [100]. In [101] the complex waveforms are separated into their real and imaginary parts and two surrogate models are constructed, with artificial neural networks (ANNs) implemented within a 4-dimensional input space to fit the coefficients from the reduced basis, omitting the empirical interpolation step.…”

Section: Surrogate Models and Related Workmentioning

confidence: 99%

Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling

Styliani-Christina¹,

Nousi²,

Passalis³

et al. 2022

Preprint

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Deep learning methods have been employed in gravitational-wave astronomy to accelerate the construction of surrogate waveforms for the inspiral of spin-aligned black hole binaries, among other applications. We demonstrate, that the residual error of an artificial neural network that models the coefficients of the surrogate waveform expansion (especially those of the phase of the waveform) has sufficient structure to be learnable by a second network. Adding this second network, we were able to reduce the maximum mismatch for waveforms in a validation set by more than an order of magnitude. We also explored several other ideas for improving the accuracy of the surrogate model, such as the exploitation of similarities between waveforms, the augmentation of the training set, the dissection of the input space, using dedicated networks per output coefficient and output augmentation. In several cases, small improvements can be observed, but the most significant improvement still comes from the addition of a second network that models the residual error. Since the residual error for more general surrogate waveform models (when e.g. eccentricity is included) may also have a specific structure, one can expect our method to be applicable to cases where the gain in accuracy could lead to significant gains in computational time.

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Section: Surrogate Models and Related Workmentioning

confidence: 99%