We investigate the role that planet detection order plays in the Kepler planet detection pipeline. The Kepler pipeline typically detects planets in order of descending signal strength (MES). We find that the detectability of transits experiences an additional 5.5% and 15.9% efficiency loss, for periods < 200 days and > 200 days respectively, when detected after the strongest signal transit in a multiple-planet system. We provide a method for determining the transit probability for multiple-planet systems by marginalizing over the empirical Kepler dataset. Furthermore, because detection efficiency appears to be a function of detection order, we discuss the sorting statistics that affect the radius and period distributions of each detection order. Our occurrence rate dataset includes radius measurement updates from the California Kepler Survey (CKS), Gaia DR2, and asteroseismology. Our population model is consistent with the results of Burke et al. (2015), but now includes an improved estimate of the multiplicity distribution. From our obtained model parameters, we find that only 4.0 ± 4.6% of solar-like GK dwarfs harbor one planet. This excess is smaller than prior studies and can be well modeled with a modified Poisson distribution, suggesting that the Kepler Dichotomy can be accounted for by including the effects of multiplicity on detection efficiency. Using our modified Poisson model we expect the average number of planets is 5.86 ± 0.18 planets per GK dwarf within the radius and period parameter space of Kepler.

We present a uniform transiting exoplanet candidate list for Campaign 5 of the K2 mission. This catalog contains 75 planets with 7 multi-planet systems (5 double, 1 triple, and 1 quadruple planet system). Within the range of our search, we find 8 previously undetected candidates with the remaining 66 candidates overlapping 51% of the Kruse et al. study that manually vet Campaign 5 candidates. In order to vet our potential transit signals, we introduce the Exoplanet Detection Identification Vetter (EDI-Vetter), which is a fully automated program able to determine if a transit signal should be labeled as a false positive or a planet candidate. This automation allows us to create a statistically uniform catalog, ideal for planet occurrence rate measurements. When tested, the vetting software is able to ensure our sample is 94.2% reliable against systematic false positives. Additionally, we inject artificial transits at the light-curve-level of the raw K2 data and find the maximum completeness of our pipeline is 70% before vetting and 60% after vetting. For convenience of future occurrence rate studies, we include measurements of stellar noise (CDPP) and the three-transit window function for each target. This study is part of a larger survey of the K2 data set and the methodology which will be applied to the entirety of the K2 data set.

Previous measurements of stellar properties for K2 stars in the Ecliptic Plane Input Catalog (EPIC;Huber et al. 2016) largely relied on photometry and proper motion measurements, with some added information from available spectra and parallaxes. Combining Gaia DR2 distances with spectroscopic measurements of effective temperatures, surface gravities, and metallicities from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) DR5, we computed updated stellar radii and masses for 26,838 K2 stars. For 195,250 targets without a LAMOST spectrum, we derived stellar parameters using random forest regression on photometric colors trained on the LAMOST sample. In total, we measured spectral types, effective temperatures, surface gravities, metallicities, radii, and masses for 222,088 A, F, G, K, and M-type K2 stars. With these new stellar radii, we performed a simple reanalysis of 299 confirmed and 517 candidate K2 planet radii from Campaigns 1-13, elucidating a distinct planet radius valley around 1.9 R ⊕ , a feature thus far only conclusively identified with Kepler planets, and tentatively identified with K2 planets. These updated stellar parameters are a crucial step in the process toward computing K2 planet occurrence rates.