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

Khan, Asad; Huerta, E. A.; Kumar, P.

doi:10.1016/j.physletb.2022.137505

Cited by 4 publications

(3 citation statements)

References 49 publications

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“…In this context, a trained ML model may be reusable as the backbone to develop another model or to fine-tune it to perform a different task, e.g. the WaveNet model [53], originally developed for text-to-speech and music generation has been adapted for classification and regression tasks in astrophysics [54,55]. Recent approaches based on 'foundation models,' [56] in which large models (sometimes containing up to 10 9 parameters) are pre-trained on unlabeled datasets and subsequently fine-tuned for downstream tasks, illustrate the need for reusability at large scale.…”

Section: Reusablementioning

confidence: 99%

FAIR AI models in high energy physics

Duarte,

Li,

Roy

et al. 2023

Mach. Learn.: Sci. Technol.

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The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

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Section: Reusablementioning

confidence: 99%

FAIR AI models in high energy physics

Duarte,

Li,

Roy

et al. 2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Recent accomplishments at the interface of physics inspired AI and supercomputing include the design of physics inspired AI architectures, training and optimization schemes that leverage thousands of GPUs [40,41]. These AI surrogates have been used to process from seconds-to years-long datasets of gravitational wave data to demonstrate that AI can be used to search for and find gravitational wave signals with an average false positive rate of one misclassification for every month of searched data [42,43].…”

Section: Introductionmentioning

confidence: 99%

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

Tian,

Huerta,

Zheng

et al. 2024

Mach. Learn.: Sci. Technol.

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We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes $(\ell, |m|)=\{(2, 2), (2, 1), (3, 3), (3, 2), (4, 4)\}$, and mode mixing effects in the $\ell = 3, |m| = 2$ harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 hours using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 hours. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

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“…Recent accomplishments at the interface of physics inspired AI and supercomputing include the design of physics inspired AI architectures, training and optimization schemes that leverage thousands of GPUs 31,32 . These AI surrogates have been used to process from seconds-to years-long datasets of gravitational wave data to demonstrate that AI can be used to search for and find gravitational wave signals with an average false positive rate of one misclassification for every month of searched data 33,34 .…”

Section: Introductionmentioning

confidence: 99%

Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection

Huerta

Tian

Zheng

2023

Preprint

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We introduce a novel method for gravitational wave detection that combines: 1) hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational wave signals; and 2) graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a detector network. These spatiotemporal-graph AI models are tested for signal detection of gravitational waves emitted by quasi-circular, non-spinning and quasi-circular, spinning, non-precessing binary black hole mergers. For the latter case, we needed a dataset of 1.2 million modeled waveforms to densely sample this signal manifold. Thus, we reduced time-to-solution by training several AI models in the Polaris supercomputer at the Argonne Leadership Supercomputing Facility within 1.7 hours by distributing the training over 256 NVIDIA A100 GPUs, achieving optimal classification performance. This approach also exhibits strong scaling up to 512 NVIDIA A100 GPUs. We then created ensembles of AI models to process data from a three detector network, namely, the advanced LIGO Hanford and Livingston detectors, and the advanced Virgo detector. An ensemble of 2 AI models achieves state-of-the-art performance for signal detection, and reports seven misclassifications per decade of searched data, whereas an ensemble of 4 AI models achieves optimal performance for signal detection with two misclassifications for every decade of searched data. Finally, when we distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, our AI ensemble is capable of processing a decade of gravitational wave data from a three detector network within 3.5 hours, i.e., 2.5×104X faster than real-time. This work underscores the importance of combining advances in AI and supercomputing to address grand challenges in big data physics experiments.

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

Cited by 4 publications

References 49 publications

FAIR AI models in high energy physics

FAIR AI models in high energy physics

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

Physics-inspired spatiotemporal-graph AI ensemble for gravitational wave detection

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