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Improving NEO Discovery Efficiency With Citizen Science. Tim Axelrod LSST EPO Scientist taxelrod@lsst.org October 1, 2013. LSST - Large Synoptic Survey Telescope. Wide Fast Deep Optical Survey 8.4 M Primary Aperture 3.5 Degree Field Of View 3.2 Billion Pixel Camera ~40 Second Cadence
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Improving NEO Discovery Efficiency With Citizen Science Tim Axelrod LSST EPO Scientist taxelrod@lsst.org October 1, 2013
LSST - Large Synoptic Survey Telescope • Wide Fast Deep Optical Survey • 8.4 M Primary Aperture • 3.5 Degree Field Of View • 3.2 Billion Pixel Camera • ~40 Second Cadence • Two 15 second exposures • Full sky coverage every few nights • Data Served and Archived • Alerts of new events • Catalogs of objects • Images • Education and Public Outreach integral to the project • Science Operations Start in 2022
LSST is a Different Kind of Telescope • An integrated survey system. The Observatory, Telescope, Camera and Data Management system are all built to support the LSST survey. There’s no PI mode, proposals, or time. • Observe the database, simultaneous investigations, data mining rather than classic observing. • The ultimate deliverable of LSST is not the telescope, nor the instruments; it’s the fully reduced data. “LSST” is the database. The “Google Index” of the Optical Sky.
Citizen Science adds Value to LSST understood data all data need human intervention machine learning cool mysteries understood data Credit: P. Gay/SIUE, S. Jacoby/LSST
How Can We Best Use Citizen Scientists for Asteroid Detection? LSST will collect roughly 1.1 million asteroid detections every night. I think this is too many for CS’s to look at every one! Instead, let’s give them a higher level task: Assist with track linkage
Citizen Science and Track Linking • The computational complexity of track linking grows exponentially with the number of asteroid detections in an image, and with the speed of their motion (particularly a problem for NEOs) • This is a major problem for LSST • Current algorithms produce many candidate tracks whose correctness is not assured • Other information can be brought to bear under human control • Real orbital dynamics • Knowledge of the Solar System population statistics • Light curves • Observations from other telescopes • With the right sort of tools, I think it is practical for citizen scientists to undertake this kind of analysis, and thereby greatly increase the efficiency of the entire man/machine system for detecting and cataloging NEOs
Track Analysis Tools for Citizen Scientists • There are parallels with another application area: Wide Area Motion Imagery (WAMI) • Tracking moving objects on the ground between images separated in space and time • Human input often required • Tools are under active development • We can learn from this community! • Goal is to have several interacting tools, each providing its own perspective on the likelihood that a track is correct • Image examiner • Lightcurve viewer • Orbit fitter • The role of the citizen scientist is to use these tools to draw a conclusion about whether a candidate track is likely real or not • Challenging, and fun!
