Topics at the interface of machine learning and high-performance computing for gravitational wave astrophysics

Multi-Messenger Astrophysics (MMA) aims to provide key insights into the origin, evolution and large scale structure of the universe by probing and combining information from different cosmic messengers; gravitational waves (GW), electromagnetic waves (EM), cosmic rays, and neutrinos. The recent direct detections by LIGO have firmly added gravitational waves (GW) to this list of cosmic messengers and thus a new era of GW astrophysics has started. However, the full potential for scientific discovery of current and future MMA surveys in the context of gravitational waves is limited by the key challenges involved in; (i) real time detection of GW signals, (ii) rapid and accurate parameter estimation from those signals, (iii) rapid modeling of numerical relativity waveforms covering the entire range of parameters of interest, and (iv) extreme computational costs and high latency of analyzing rapidly increasing volumes of observational data. In this thesis, we present a framework that combines deep learning (DL) and high performance computing (HPC) to contend with many of the existing challenges of real-time MMA. In particular; (i) we present an ensemble of deep learning models that can detect gravitational wave events from compact binary coalescences in real-time. When deployed on archival data from LIGO's O2 and O3 runs, this framework is able to identify all events with previously confirmed detections, and has a false alarm rate of 1 misclassification for every 2.7 days of searched data. We then develop a workflow to demonstrate the scaling of such an analysis on an HPC platform to reduce time to insight. (ii) We also study the efficacy of deep learning models to estimate parameters from waveforms of spinning, non-precessing bninary black hole mergers in the absence of noise, the ability of these models to handle parameter space degeneracies, and the effects of including higher order modes on the accuracy of parameter estimations. (iii) Finally, we test the ability of deep learning to model full numerical relativity waveforms, as an alternative to surrogate modeling and Gaussian emulation methods. In the last chapter of this thesis, we present a deep learning model that can efficiently and accurately forecast the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers. These results indicate that combining deep learning algorithms and high performance computing is a promising new paradigm that can help accelerate scientific discovery in the era of multi-messenger astrophysics.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Khan, Asad

Contributors dc:contributor

• Huerta, Eliu A.
• Seidel, Harry Edward
• Allen, Gabrielle D.
• Zhao, Zhizhen
• Cooper, S. Lance

• General Relativity
• Gravitational Waves
• LIGO
• Multimessenger Astrophysics
• Artificial Intelligence
• High-Performance Computing

Statement dc:rights

• Copyright 2022 Asad Khan

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115341

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115341