INSPIRE: Teaming Citizen Science with Machine Learning to Deepen LIGO's View of the Cosmos

INSPIRE: Teaming Citizen Science with Machine Learning to Deepen LIGO's View of the Cosmos

This project (INSPIRE 15-47880) has developed a citizen science system--Gravity Spy (http://gravityspy.org/)--to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO), the most complicated experiment ever undertaken in gravitational physics. LIGO has opened up the window of gravitational wave observations on the Universe. However, the high detector sensitivity needed for astrophysical discoveries makes aLIGO very susceptible to non-cosmic artifacts and noise that must be identified and separated from cosmic signals. Teaching computers to identify and morphologically classify these artifacts in detector data is exceedingly difficult. Human eyesight is a proven tool for classification, but the aLIGO data streams from approximately 30,000 sensors and monitors easily overwhelm a single human. This research will address these problems by coupling human classification with a machine learning model that learns from the citizen scientists and also guides how information is provided to participants. A novel feature of this system will be its reliance on volunteers to discover new glitch classes, not just use existing ones. The project includes research on the human-centered computing aspects of this sociocomputational system, and thus can inspire future citizen science projects that do not merely exploit the labor of volunteers but engage them as partners in scientific discovery. Therefore, the project will have substantial educational benefits for the volunteers, who will gain a good understanding on how science works, and will be a part of the excitement of opening up a new window on the universe. The project is joint with Vassiliki Kalogera (Northwestern University), Joshua Smith (Cal State Fullerton), Shane Larson (Northwestern University) and Laura Trouille (Adler Planetarium), with involvement at Syracuse by Kevin Crowston and Carsten Ă˜sterlund. For more detail, see http://ciera.northwestern.edu/Research/Gravity_Spy.php

Publications from this grant are listed below.

crowston

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science crowston

Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training

Teaching Citizen Scientists to Categorize Glitches using Machine-Learning-Guided Training
Attachment Size
MLGT-preprint.pdf 2.43 MB
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Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning

Classifying the unknown: Discovering novel gravitational-wave detector glitches using similarity learning crowston

Folksonomies to support coordination and coordination of folksonomies

Folksonomies to support coordination and coordination of folksonomies
Attachment Size
ECSCW-Paper-Final.pdf 2.14 MB
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Knowledge Tracing to Model Learning in Online Citizen Science Projects

Knowledge Tracing to Model Learning in Online Citizen Science Projects crowston

Shifting forms of Engagement: Volunteer Learning in Online Citizen Science

Shifting forms of Engagement: Volunteer Learning in Online Citizen Science
Attachment Size
3392841.pdf 1.13 MB
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Linguistic adoption in online citizen science: A structurational perspective

Linguistic adoption in online citizen science: A structurational perspective
Attachment Size
Linguistic Adoption (ICIS) final.pdf 3.07 MB
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