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We present a methodology based on the implementation of a fully connected neural network algorithm to estimate the temporal evolution of the high-frequency gravitational wave emission for a core collapse supernova (CCSN). For this study, we selected a fully connected deep neural network (DNN) regression model because it can learn both linear and nonlinear relationships between the input and output data, it is more appropriate for handling large-dimensional input data, and it offers high performance at a low computational cost. To train the Machine Learning (ML) algorithm, we construct a training dataset using synthetic waveforms, and several CCSN waveforms are used to test the algorithm. We performed a first-order estimation of the high-frequency gravitational wave emission on real interferometric LIGO data from the second half of the third observing run (O3b) with a two detector network (L1 and H1). The relative error associated with the estimate of the slope of the resonant frequency versus time for the GW from CCSN signals is within 13% for the tested candidates included in this study up to different Galactic distances (1.0, 2.3, 3.1, 4.3, 5.4, 7.3, and 10 kpc). This method is, to date, the best estimate of the temporal evolution of the high-frequency emission in real interferometric data. Our methodology of estimation can be used in future studies focused on physical properties of the progenitor. The distances where comparable performances could be achieved for Einstein Telescope and Cosmic Explorer roughly rescale with the noise floor improvements.
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This content will become publicly available on April 29, 2026
Parameter estimation from the core-bounce phase of rotating core collapse supernovae in real interferometer noise
Abstract We develop and characterize a parameter estimation methodology for rotating core collapse supernovae based on the gravitational wave core bounce phase and real detector noise. Expanding on the evidence from numerical simulations for the deterministic nature of this gravitational wave emission and about the dependence on the ratio $$\beta$$ between rotational kinetic to potential energy, we propose an analytical model for the core bounce component which depends on $$\beta$$ and one phenomenological parameter. We validate the goodness of the model with a pool of representative waveforms. We use the fitting factor adopted in compact coalescing binary searches as a metric to quantify the goodness of the analytical model and the template bank generated by the model presents an average accuracy of 94.4\% when compared with the numerical simulations and is used as the basis for the work. The error for a matched filter frequentist parameter estimation of $$\beta$$ is evaluated. The results obtained considering real interferometric noise and a waveform at a distance of 10 kpc and optimal orientation, for one standard deviation estimation error of the rotation parameter \(\beta\) lie in the range of \(10^{-2}\) to \(10^{-3}\) as \(\beta\) increases. The results are also compared to the scenario where Gaussian recolored data is employed. The analytical model also allows for the first time, to compute theoretical minima in the error for $$\beta$$ for any type of estimator. Our analysis indicates that the presence of rotation would be detectable at 0.5 Mpc for third generation interferometers like CE or ET.
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- PAR ID:
- 10590585
- Publisher / Repository:
- Classical and Quantum Gravity
- Date Published:
- Journal Name:
- Classical and Quantum Gravity
- ISSN:
- 0264-9381
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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