Use of AI in Astronomy
Northwestern's Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) is home to several groups at the forefront of AI research in astronomy. Vicky Kalogera's group uses AI approaches to explore massive binary systems lives, properties, and fates. Adam Miller's group works at the intersection of wide-field time-domain astronomical surveys and science, and uses AI techniques in the era of big data astronomy. Jason Wang's group is developing AI image reconstruction techniques for the next generation of exoplanet imaging instruments. Gravity Spy, developed at CIERA, combines the crowd-sourcing power of citizen science with machine learning to classify glitches in gravitational-wave (GW) data. Glitches are transient bursts of noise, of a wide range of origins, which impact our ability to analyze GW data.
Gravity Spy uses a convolutional neural net to classify glitches based on spectrogram images; citizen scientists perform a similar task, expanding our training set, and aiding classification of uncertain glitches. The machine learning algorithm allows us to rapidly classify glitches, producing near real-time reports for assessing candidate GW events, and enabling us to compile a large glitch database for study by detector experts. The Gravity Spy glitch classes are determined by experts, but as new detectors are commissioned, new glitches can arise. Gravity Spy empowers its citizen scientists to identify new glitch classes by allowing citizens to search for morphologically similar glitches to an interesting example. Volunteers have already identified multiple new glitch types, demonstrating that the public can make meaningful contributions to cutting-edge science.
Learn more about CIERA's use of AI in astronomy
Learn more about Northwestern's use of AI in research exploration
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