Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources. (arXiv:2105.06479v1 [astro-ph.IM])

We live in momentous times. The science community is empowered with an
arsenal of cosmic messengers to study the Universe in unprecedented detail.
Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a
wide range of wavelengths and time scales. Combining and processing these
datasets that vary in volume, speed and dimensionality requires new modes of
instrument coordination, funding and international collaboration with a
specialized human and technological infrastructure. In tandem with the advent
of large-scale scientific facilities, the last decade has experienced an
unprecedented transformation in computing and signal processing algorithms. The
combination of graphics processing units, deep learning, and the availability
of open source, high-quality datasets, have powered the rise of artificial
intelligence. This digital revolution now powers a multi-billion dollar
industry, with far-reaching implications in technology and society. In this
chapter we describe pioneering efforts to adapt artificial intelligence
algorithms to address computational grand challenges in Multi-Messenger
Astrophysics. We review the rapid evolution of these disruptive algorithms,
from the first class of algorithms introduced in early 2017, to the
sophisticated algorithms that now incorporate domain expertise in their
architectural design and optimization schemes. We discuss the importance of
scientific visualization and extreme-scale computing in reducing
time-to-insight and obtaining new knowledge from the interplay between models
and data.



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