LIGO Seminar

Being the most complicated and sensitive experiment ever undertaken in gravitational physics, aLIGO is susceptible to various sources of environmental and instrumental noise that hinder the search for more gravitational waves. Of particular concern are transient, non-Gaussian noise features known as glitches. Glitches can mimic true astrophysical gravitational waves and occur at a high enough frequency to be  coherent between the two detectors. The proper classification and characterization of glitches is paramount in optimizing aLIGO's ability to detect gravitational waves.

To tackle this problem, we created Gravity Spy an innovative,interdisciplinary
project hosted by Zooniverse that combines aLIGO detector characterization, citizen science, machine learning, and social science. In this project, citizen scientists and computers work together in a symbiotic relationship: volunteers classify triggers from the aLIGO data steam that are constantly updated as aLIGO takes in new data, and these classifications are used to train machine learning algorithms which proceed to classify the bulk of aLIGO data and feed questionable glitches back to the users.

In this talk, I will discuss the workflow and first two years' results of the Gravity Spy project and how Gravity Spy can be of use to aLIGO's future observing runs and highlight the potential of such citizen science projects in promoting nascent fields such as gravitational wave astrophysics.