85 citations · 408 across the 11 of their papers we have counts for
11 papers
Inference-optimized AI and high performance computing for gravitational wave detection at scale
Pranshu Chaturvedi, Asad Khan, Minyang Tian +2
We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V10…
Interpreting a Machine Learning Model for Detecting Gravitational Waves
Mohammadtaher Safarzadeh, Asad Khan, E. A. Huerta +1
We describe a case study of translational research, applying interpretability techniques developed for computer vision to machine learning models used to search for and find gravit…
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
Asad Khan, E. A. Huerta, Prayush Kumar
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. W…
Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
Asad Khan, E. A. Huerta, Huihuo Zheng
We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that d…
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
E. A. Huerta, Asad Khan, Xiaobo Huang +10
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astro…
Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
Wei Wei, Asad Khan, E. A. Huerta +2
We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent ne…
Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
Asad Khan, E. A. Huerta, Arnav Das
The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form,…
Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
E. A. Huerta, Asad Khan, Edward Davis +9
Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable sc…
Enabling real-time multi-messenger astrophysics discoveries with deep learning
E. A. Huerta, Gabrielle Allen, Igor Andreoni +57
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that…
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
Gabrielle Allen, Igor Andreoni, Etienne Bachelet +45
This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrop…
Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
Asad Khan, E. A. Huerta, Sibo Wang +3
The scale of ongoing and future electromagnetic surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include citizen science campai…