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1. Characterizing the temporal evolution of the high-frequency gravitational wave emission for a core collapse supernova with laser interferometric data: A neural network approach

   Casallas-Lagos, A; Antelis, J; Moreno, C; Zanolin, M; Mezzacappa, A; Szczepańczyk, M (October 2023, Physical review D)

   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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2. Parameter estimation from the core-bounce phase of rotating core collapse supernovae in real interferometer noise

   https://doi.org/10.1088/1361-6382/add235  

   Villegas, Laura O; Moreno, Claudia; Pajkos, Michael A; Zanolin, Michele; Antelis, Javier M (April 2025, Classical and Quantum Gravity)

   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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3. Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications

   https://doi.org/10.1088/1361-6382/acb633  

   Glanzer, J.; Banagiri, S.; Coughlin, S. B.; Soni, S.; Zevin, M.; Berry, C. P. L.; Patane, O.; Bahaadini, S.; Rohani, N.; Crowston, K.; et al (February 2023, Classical and Quantum Gravity)

   Abstract Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory. 

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4. Statistically-informed deep learning for gravitational wave parameter estimation

   https://doi.org/10.1088/2632-2153/ac3843  

   Shen, Hongyu; Huerta, E A; O’Shea, Eamonn; Kumar, Prayush; Zhao, Zhizhen (November 2021, Machine Learning: Science and Technology)

   Abstract We introduce deep learning models to estimate the masses of the binary components of black hole mergers, ( m 1 , m 2 ) , and three astrophysical properties of the post-merger compact remnant, namely, the final spin, a f , and the frequency and damping time of the ringdown oscillations of the fundamental ℓ = m = 2 bar mode, ( ω R , ω I ) . Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters ( m 1 , m 2 , a f , ω R , ω I ) of five binary black holes: GW150914 , GW170104 , GW170814 , GW190521 and GW190630 . We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. This neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science . 

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5. Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks

   https://doi.org/10.1038/s42003-023-04914-y  

   Movahhedi, Mohammadreza; Liu, Xin-Yang; Geng, Biao; Elemans, Coen; Xue, Qian; Wang, Jian-Xun; Zheng, Xudong (May 2023, Communications Biology)

   Abstract Tissue dynamics play critical roles in many physiological functions and provide important metrics for clinical diagnosis. Capturing real-time high-resolution 3D images of tissue dynamics, however, remains a challenge. This study presents a hybrid physics-informed neural network algorithm that infers 3D flow-induced tissue dynamics and other physical quantities from sparse 2D images. The algorithm combines a recurrent neural network model of soft tissue with a differentiable fluid solver, leveraging prior knowledge in solid mechanics to project the governing equation on a discrete eigen space. The algorithm uses a Long-short-term memory-based recurrent encoder-decoder connected with a fully connected neural network to capture the temporal dependence of flow-structure-interaction. The effectiveness and merit of the proposed algorithm is demonstrated on synthetic data from a canine vocal fold model and experimental data from excised pigeon syringes. The results showed that the algorithm accurately reconstructs 3D vocal dynamics, aerodynamics, and acoustics from sparse 2D vibration profiles. 

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