Anil Ananthaswamy, writing for Scientific American:
[T]the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. Promising signals trigger an internal alert; those that survive additional scrutiny trigger a public alert so that the global astronomy community can look for electromagnetic and neutrino counterparts.
Template matching is so computationally intensive that, for gravitational waves produced by mergers, astronomers use only four attributes of the colliding cosmic objects (the masses of both and the magnitudes of their spins) to make detections in real time. From there, LIGO scientists spend hours, days or even weeks performing more processing offline to further refine the understanding of a signal’s sources, a task called parameter estimation.
Seeking ways to make that labyrinthine process faster and more computationally efficient, in work published in 2018, Huerta and his research group at NCSA turned to machine learning. Specifically, Huerta and his then graduate student Daniel George pioneered the use of so-called convolutional neural networks (CNNs), which are a type of deep-learning algorithm, to detect and decipher gravitational-wave signals in real time.
Faced with a Data Deluge, Astronomers Turn to Automation
And they learned a bit about what the neural networks are seeing:
For Ntampaka, these results suggest that machine-learning systems are not entirely immune to interpretation. “It’s a misunderstanding within the community that they only can be black boxes,” she says. “I think interpretability is on the horizon. It’s coming. We are starting to be able to do it now.” But she also acknowledges that had her team not already known the underlying physics connecting the x-ray emissions from galaxy clusters to their mass, it might not have figured out that the neural network was excising the cores from its analysis.