Planet Hunter

A 2012 peer-reviewed journal article titled “Planet Hunters: the first two planet candidates identified by the public using the Kepler public archive data”, published in the Monthly Notices of the Royal Astronomical Society, documents the initial success of Planet Hunters, a citizen science project integrated into the Zooniverse network.

The project crowdsources the analysis of public time-series photometry data from NASA’s Kepler mission, leveraging human pattern recognition to find planetary transits that automated algorithms might miss.

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Key Findings of the Paper

• Two New Discoveries: Within the first month of the project’s launch, human volunteers successfully identified two new exoplanet candidates from the Kepler Quarter 1 (Q1) data:
  • KIC 10905746: A planet candidate with an orbital period of approx 9.88 and a radius 2.65 times that of Earth 2.65 R
  • KIC 6185331: A larger planet candidate with an orbital period of approx 49.77 days and a radius of 8.05 R
• Complementary to Algorithms: While both targets had initially tripped NASA’s automated Transit Planet Search (TPS) software, they were dropped or failed to progress through the official pipeline due to data-fitting issues or bad stellar classification. Human eyes successfully looked past background stellar variability to flag the transits.
• Rigorous Follow-Up: The scientific team verified these public discoveries using high-resolution spectroscopy via Keck HIRES, pixel centroid offset analysis, and adaptive optics imaging to rule out common false positives like background eclipsing binaries.

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Bill Kandiliotis participated as one of the citizen scientist volunteers for the Planet Hunters project.

Specifically, his role involved manually reviewing Kepler light curves and flagging the specific transit events (the slight dips in a star’s brightness caused by a planet crossing in front of it) for the light curves discussed in the paper.

Because the human brain is uniquely adept at identifying visual anomalies and archetypal patterns amidst noisy background data, his classifications directly contributed to the raw discovery data. To acknowledge this vital frontline contribution, the authors explicitly credited him by name in the paper’s Additional Information / Acknowledgments section, noting that the work would have been impossible without the dedication of volunteers like him.

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