This INSPIRE award is partially funded by the Cyber-Human Systems Program in the Division of Information and Intelligent Systems in the Directorate for Computer Science and Engineering, the Gravitational Physics Program in the Division of Physics in the Directorate for Mathematical and Physical Sciences, and the Office of Integrative Activities.
This innovative project will develop a citizen science system to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO), the most complicated experiment ever undertaken in gravitational physics. Before the end of this decade it will open up the window of gravitational wave observations on the Universe. However, the high detector sensitivity needed for astrophysical discoveries makes aLIGO very susceptible to noncosmic 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.
This is an innovative, interdisciplinary collaboration between the existing LIGO, at the time it is being technically enhanced, and Zooniverse, which has fielded a workable crowdsourcing model, currently involving over a million people on 30 projects. The work will help aLIGO to quickly identify noise and artifacts in the science data stream, separating out legitimate astrophysical events, and allowing those events to be distributed to other observatories for more detailed source identification and study. This project will also build and evaluate an interface between machine learning and human learning that will itself be an advance on current methods. It can be depicted as a loop: (1) By sifting through enormous amounts of aLIGO data, the citizen scientists will produce a robust "gold standard" glitch dataset that can be used to seed and train machine learning algorithms that will aid in the identification task. (2) The machine learning protocols that select and classify glitch events will be developed to maximize the potential of the citizen scientists by organizing and passing the data to them in more effective ways. The project will experiment with the task design and workflow organization (leveraging previous Zooniverse experience) to build a system that takes advantage of the distinctive strengths of the machines (ability to process large amounts of data systematically) and the humans (ability to identify patterns and spot discrepancies), and then using the model to enable high quality aLIGO detector characterization and gravitational wave searches
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TEAM MEMBERS:
Vassiliki KalogeraAggelos KatsaggelosKevin CrowstonLaura TrouilleJoshua SmithShane LarsonLaura Whyte
This Advancing Informal Science Learning Pathways project, Using Technology to Research After Class (UTRAC), explores whether a combination of technology (e.g., iPad-enabled sensors, web-based inquiry-focused portal) and facilitated visits improves learning outcomes for rural and Native American elementary-age youth in after school programs. Expected outcomes include improved engagement, knowledge, skills, and attitudes toward science, technology, engineering, and math (STEM). Project goals include promoting STEM learning through science inquiry activities keyed to specific Next Generation Science Standards as well as improving how technology can be used to enhance learning outcomes in afterschool programs. The experimental design of this project - testing the effects of physical or virtual facilitation visits on learning outcomes - will lead to improvements in STEM learning outcomes among rural and underrepresented students. This project will employ several innovations in utilizing technology to teach STEM topics including: (i) hands-on, real-time, crowd sourced data collected by participants in their schoolyards; (ii) a pedagogic emphasis on communication of schoolyard data among and between participants; (iii) testing of motivational incentives; and (iv) partnerships between after school providers, preservice teachers, and university researchers as facilitators. The entire process will be modularized so that it can be modified in terms of place, STEM topic or student cohort. The topic focus of the project -- Life Under Snow -- is relevant to participating students, as Montana school playgrounds lie blanketed under snow for the majority of the school year; it includes elements of snow science, carbon cycle science, and a combination at the intersection of three recent literacy initiatives (e.g., Earth Science, Climate, or Energy). UTRAC will pilot and evaluate facilitated snow science/carbon cycle science activities that couple real-time schoolyard data with tools patterned after those available through WISE (Web-based Inquiry Science Environment; wise.berkeley.edu). Participants will collect and compare data with other youth participants, and researchers will use formative assessments to define interventions with potential to maximize student engagement and learning improvements among underserved youth. The project will advance understanding of informal education's potential to improve STEM engagement, knowledge, skills and attitudes by quantifying how - and to what extent - youth engage with emerging technologies iPad-enabled sensors, and crowdsourcing and visualization tools. The deliverables include a quantifying metric for learning outcomes, a training model for the iPad sensors and web application, an orientation kit, a social media portal, and database for the measurements.