Planet Hunters TESS II: Findings from the first two years of TESS
Nora L. Eisner, Oscar Barragán, Chris Lintott, Suzanne Aigrain, Belinda Nicholson, Tabetha S. Boyajian, Steve B. Howell, Cole Johnston, Ben Lakeland, Grant Miller, Adam McMaster, Hannu Parviainen, Emily J. Safron, Megan E. Schwamb, Laura Trouille, Sophia Vaughan, Norbert Zicher, Campbell Allen, Sarah Allen, Mark Bouslog, Cliff Johnson, Molly N. Simon, Zach Wolfenbarger, Elisabeth M. L. Baeten, David M. Bundy, Tony Hoffman
We present the results from the first two years of the Planet Hunters TESS citizen science project, which identifies planet candidates in the TESS data by engaging members of the general public. Over 22,000 citizen scientists from around the world visually inspected the first 26 Sectors of TESS data in order to help identify transit-like signals. We use a clustering algorithm to combine these classifications into a ranked list of events for each sector, the top 500 of which are then visually vetted by the science team. We assess the detection efficiency of this methodology by comparing our results to the list of TESS Objects of Interest (TOIs) and show that we recover 85 % of the TOIs with radii greater than 4 Earth radii and 51 % of those with radii between 3 and 4 Earth radii. Additionally, we present our 90 most promising planet candidates that had not previously been identified by other teams, 73 of which exhibit only a single transit event in the TESS light curve, and outline our efforts to follow these candidates up using ground-based observatories. Finally, we present noteworthy stellar systems that were identified through the Planet Hunters TESS project.
Comments: Accepted for publication in MNRAS (22 pages, 12 figures, 3 tables)
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Solar and Stellar Astrophysics (astro-ph.SR)
Cite as: arXiv:2011.13944 [astro-ph.EP] (or arXiv:2011.13944v1 [astro-ph.EP] for this version)
Submission history
From: Nora L. Eisner Miss
[v1] Fri, 27 Nov 2020 19:00:02 UTC (24,611 KB)
