Combining effective-one-body accuracy and reduced-order-quadrature speed for binary neutron star merger parameter estimation with machine learning

Tissino, Jacopo; Carullo, G.; Breschi, M.; Gamba, R.; Schmidt, S.; Bernuzzi, Sebastiano

doi:10.1103/physrevd.107.084037

Cited by 9 publications

(1 citation statement)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The role of deep learning (DL) for GW data analysis is already relevant and is bound to become increasingly so, as GW astronomy fully unfolds (see [29,30] and references therein). Current applications are not only limited to accelerating parameter estimation but also include efforts to develop detection methods [31][32][33], to improve signal quality [34,35], to accelerate waveform generation [36,37] as well as to simulate noise transients in GW detectors [38,39]. There are further applications to broader black hole physics beyond GW astronomy, such as BH evaporation [40], calculation of quasinormal modes [41], and proposals for geometry-inspired neural network models that mirror the properties of black holes (such as an area law for entropy) [42].…”

Section: Introductionmentioning

confidence: 99%

Comparison of neural network architectures for feature extraction from binary black hole merger waveforms

Gramaxo Freitas,

Calderón Bustillo,

Font

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binaryblack-hole mergers. We build datasets of 100,000 elements for each of four diﬀerent waveform models (or approximants) in order to test how approximant choice aﬀects feature extraction. Our choices include SEOBNRv4P and IMRPhenomPv3, which contain only the dominant quadrupole emission mode, alongside IMRPhenomPv3HM and NRHybSur3dq8, which also account for high-order modes. Each dataset element is injected into detector noise corresponding to the third observing run of the LIGO-Virgo-KAGRA (LVK) collaboration. We identify the Temporal Convolutional Network (TCN) architecture as the overall best performer in terms of training and validation losses and absence of overﬁtting to data. Comparison of results between datasets shows that the choice of waveform approximant for the creation of a dataset conditions the feature extraction ability of a trained network. Hence, care should be taken when building a dataset for the training of neural networks, as certain approximants may result in better network convergence of evaluation metrics. However, this performance does not necessarily translate to data which is more faithful to numerical relativity simulations. We also apply this network on actual signals from LVK runs, ﬁnding that its feature-extracting performance can be eﬀective on real data.

show abstract