Biases in Planet Occurrence Caused by Unresolved Binaries in Transit Surveys

L. G. Bouma, Kento Masuda, Joshua N. Winn

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Wide-field surveys for transiting planets, such as the NASA Kepler and TESS missions, are usually conducted without knowing which stars have binary companions. Unresolved and unrecognized binaries give rise to systematic errors in planet occurrence rates, including misclassified planets and mistakes in completeness corrections. The individual errors can have different signs, making it difficult to anticipate the net effect on inferred occurrence rates. Here, we use simplified models of signal-to-noise limited transit surveys to try and clarify the situation. We derive a formula for the apparent occurrence rate density measured by an observer who falsely assumes all stars are single. The formula depends on the binary fraction, the mass function of the secondary stars, and the true occurrence of planets around primaries, secondaries, and single stars. It also takes into account the Malmquist bias by which binaries are over-represented in flux-limited samples. Application of the formula to an idealized Kepler-like survey shows that for planets larger than 2 R , the net systematic error is of order 5%. In particular, unrecognized binaries are unlikely to be the reason for the apparent discrepancies between hot-Jupiter occurrence rates measured in different surveys. For smaller planets the errors are potentially larger: the occurrence of Earth-sized planets could be overestimated by as much as 50%. We also show that whenever high-resolution imaging reveals a transit host star to be a binary, the planet is usually more likely to orbit the primary star than the secondary star.

Original languageEnglish (US)
Article number244
JournalAstronomical Journal
Issue number6
StatePublished - Jun 2018

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


  • Planets and satellites: detection
  • Surveys
  • methods: data analysis


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