2020AJ....160..200B

The Kepler DR25 planet candidate catalog was produced using an automated method of planet candidate identification based on various tests. These tests were tuned to obtain a reasonable but arbitrary balance between catalog completeness and reliability. We produce new catalogs with differing balances of completeness and reliability by varying these tests, and study the impact of these alternative catalogs on occurrence rates. We find that if there is no correction for reliability, different catalogs give statistically inconsistent occurrence rates, while if we correct for both completeness and reliability, we get statistically consistent occurrence rates. This is a strong indication that correction for completeness and reliability is critical for the accurate computation of occurrence rates. Additionally, we find that this result is the same whether using Bayesian Poisson-likelihood Markov Chain Monte Carlo or Approximate Bayesian Computation methods. We also examine the use of a Robovetter disposition score cut as an alternative to reliability correction, and find that while a score cut does increase the reliability of the catalog, it is not as accurate as performing a full reliability correction. We get the same result when performing a reliability correction with and without a score cut. Therefore removing low-score planets removes data without providing any advantage, and should be avoided when possible. We make our alternative catalogs publicly available, and propose that these should be used as a test of occurrence rate methods, with the requirement that a method should provide statistically consistent occurrence rates for all these catalogs.