A13: Deep Learning with HPC Simulations for Extracting
Hidden Signals: Detecting Gravitational Waves
SessionPoster Reception
Author
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionWe introduce Deep Filtering, new machine learning
method for end-to-end time-series signal processing,
which combines two deep one-dimensional convolutional
neural networks for classification and regression to
detect and characterize signals much weaker than the
background noise. We trained this method with a novel
curriculum learning scheme on data derived from HPC
simulations and applied it for gravitational wave
analysis specifically for mergers of black holes and
demonstrated that it significantly outperforms
conventional machine learning techniques, is far more
efficient than matched-filtering, offering several
orders-of-magnitude speed-up, allowing real-time
processing of raw big data with minimal resources, and
extends the range of detectable signals. This initiates
a new paradigm for scientific research which employs
massively-parallel numerical simulations to train
artificial intelligence algorithms that exploit emerging
hardware architectures such as deep-learning-optimized
GPUs. Our approach offers a unique framework to enable
coincident detection campaigns of gravitational wave
sources and their electromagnetic counterparts.
