Deep dive for dark matter may aid all of data science

Deep dive for dark matter may aid all of data science

A Rice University scientist and his colleagues are booting their search for dark matter into a study they hope will enhance all of data science.

Rice astroparticle physicist Christopher Tunnell and his team have received a $1 million National Science Foundation (NSF) grant to reimagine data science techniques and help push data-intensive physical sciences past the tipping point to discovery.

Experiments in the physical sciences are starting to produce thousands of terabytes of data, Tunnell said. “These datasets are fundamentally different from large datasets of everyday photos, text or video,” he said. “Ours relate to experiences of the natural world that only highly specialized instruments and sensors can ‘see.'”

In tackling this class of problem, the two-year project aims to influence the way data scientists use machine and deep learning in bioinformatics, computational biology, materials science and environmental sciences. Tunnell said the goal is to support these physical science communities through a “domain-enhanced” data science institute.“In large astroparticle data sets, we often look for the faintest signals that anyone has ever attempted to measure,” said Tunnell, an assistant professor of physics and astronomy and computer science and lead investigator on the project.

“Science is incremental,” he said, explaining the domain-enhanced approach. “We have spent decades building up mankind’s most precise physical theories, which provide the foundation for these measurements. When using machine learning in this realm, the machine has to learn through its own ‘Phys 101.’ But the great artificial intelligence advancements of the last decade have been mostly in computer vision and natural language processing with a muted impact in physical sciences.”