AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

• URL: http://arxiv.org/abs/2112.07669v1
• Date: Mon, 13 Dec 2021 19:00:00 GMT
• Title: AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers
• Authors: Asad Khan, E.A. Huerta
• Abstract summary: We learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We train AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8. We obtain deterministic and probabilistic estimates of the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms.
• Score: 1.7056768055368385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to $\ell \leq 4$ and $(5,5)$, except for $(4,0)$ and $(4,1)$, that describe binaries with mass-ratios $q\leq8$ and individual spins $s^z_{\{1,2\}}\in[-0.8, 0.8]$. We use our AI models to obtain deterministic and probabilistic estimates of the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. Our studies indicate that AI provides informative estimates for these physical parameters. This work marks the first time AI is capable of characterizing this high-dimensional signal manifold. Our AI models were trained within 3.4 hours using distributed training on 256 nodes (1,536 NVIDIA V100 GPUs) in the Summit supercomputer.

Related papers

• AI forecasting of higher-order wave modes of spinning binary black hole mergers [0.0]
  The model forecasts the waveform evolution from the pre-merger phase through the ringdown. We trained the model on 14,440,761 waveforms, completing the training in 15 hours using 16 NVIDIA A100 GPUs in the Delta supercomputer. We conducted interpretability studies to elucidate the waveform features utilized by our transformer model to produce accurate predictions.
  arXiv  Detail & Related papers  (2024-09-05T18:00:11Z)
• Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
  We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$. We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
  arXiv  Detail & Related papers  (2024-01-17T18:59:38Z)
• AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers [0.36832029288386137]
  We train AIs with 2.4 million waveforms that describe quasi-temporal, spinning, non-precessing binary black hole masses. We then use transfer to create learning predictors that estimate the total mass of potential binary black holes. This is the first AI ensemble designed to search for and find the higher gravitational order gravitational wave mode signals.
  arXiv  Detail & Related papers  (2023-09-29T18:00:03Z)
• Geometry-Informed Neural Operator for Large-Scale 3D PDEs [76.06115572844882]
  We propose the geometry-informed neural operator (GINO) to learn the solution operator of large-scale partial differential equations. We successfully trained GINO to predict the pressure on car surfaces using only five hundred data points.
  arXiv  Detail & Related papers  (2023-09-01T16:59:21Z)
• Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers [0.2999888908665658]
  We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers. These AI models combine hybrid dilated neural networks to accurately model both short- and long-range temporal information of gravitational waves.
  arXiv  Detail & Related papers  (2023-06-27T18:09:15Z)
• Neural Inference of Gaussian Processes for Time Series Data of Quasars [72.79083473275742]
  We introduce a new model that enables it to describe quasar spectra completely. We also introduce a new method of inference of Gaussian process parameters, which we call $textitNeural Inference$. The combination of both the CDRW model and Neural Inference significantly outperforms the baseline DRW and MLE.
  arXiv  Detail & Related papers  (2022-11-17T13:01:26Z)
• Predicting the Stability of Hierarchical Triple Systems with Convolutional Neural Networks [68.8204255655161]
  We propose a convolutional neural network model to predict the stability of hierarchical triples. All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.
  arXiv  Detail & Related papers  (2022-06-24T17:58:13Z)
• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers [1.4438155481047366]
  We present a deep-learning artificial intelligence model capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms. We harnessed the Theta supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms.
  arXiv  Detail & Related papers  (2021-10-13T18:14:52Z)
• A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
  We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems. We numerically demonstrate that the MNIST image dataset satisfies such conditions. We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
  arXiv  Detail & Related papers  (2021-07-19T23:41:03Z)
• Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers [4.43457632632169]
  We introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes. We find that the convergence of high performance computing and physics-inspired optimization algorithms enable an accurate reconstruction of the mass-ratio and individual spins of binary black hole mergers.
  arXiv  Detail & Related papers  (2020-04-20T18:00:02Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.