New transiting planet candidates are identified in 16 months (2009 May-2010 of data from the Kepler spacecraft. Nearly 5000 periodic transit-like signals are vetted against astrophysical and instrumental false positives yielding 1108 viable new planet candidates, bringing the total count up to over 2300. Improved vetting metrics are employed, contributing to higher catalog reliability. Most notable is the noise-weighted robust averaging of multiquarter photo-center offsets derived from difference image analysis that identifies likely background eclipsing binaries. Twenty-two months of photometry are used for the purpose of characterizing each of the candidates. Ephemerides (transit epoch, T 0 , and orbital period, P) are tabulated as well as the products of light curve modeling: reduced radius (R P /R ), reduced semimajor axis (d/R ), and impact parameter (b). The largest fractional increases are seen for the smallest planet candidates (201% for candidates smaller than 2 R ⊕ compared to 53% for candidates larger than 2 R ⊕ ) and those at longer orbital periods (124% for candidates outside of 50 day orbits versus 86% for candidates inside of 50 day orbits). The gains are larger than expected from increasing the observing window from 13 months (Quarters 1-5) to 16 months (Quarters 1-6) even in regions of parameter space where one would have expected the previous catalogs to be complete. Analyses of planet frequencies based on previous catalogs will be affected by such incompleteness. The fraction of all planet candidate host stars with multiple candidates has grown from 17% to 20%, and the paucity of short-period giant planets in multiple systems is still evident. The progression 1The Astrophysical Journal Supplement Series, 204:24 (21pp), 2013 February Batalha et al. toward smaller planets at longer orbital periods with each new catalog release suggests that Earth-size planets in the habitable zone are forthcoming if, indeed, such planets are abundant.

We report on the orbital architectures of Kepler systems having multiple planet candidates identified in the analysis of data from the first six quarters of Kepler data and reported by Batalha et al. (2013). These data show 899 transiting planet candidates in 365 multiple-planet systems and provide a powerful means to study the statistical properties of planetary systems. Using a generic massradius relationship, we find that only two pairs of planets in these candidate systems (out of 761 pairs total) appear to be on Hill-unstable orbits, indicating ∼ 96% of the candidate planetary systems are correctly interpreted as true systems. We find that planet pairs show little statistical preference to be near mean-motion resonances. We identify an asymmetry in the distribution of period ratios near firstorder resonances (e.g., 2:1, 3:2), with an excess of planet pairs lying wide of resonance and relatively few lying narrow of resonance. Finally, based upon the transit duration ratios of adjacent planets in each system, we find that the interior planet tends to have a smaller transit impact parameter than the exterior planet does. This finding suggests that the mode of the mutual inclinations of planetary orbital planes is in the range 1.0• -2.2• , for the packed systems of small planets probed by these observations.

We report the distribution of planets as a function of planet radius, orbital period, and stellar effective temperature for orbital periods less than 50 days around Solar-type (GK) stars. These results are based on the 1,235 planets (formally "planet candidates") from the Kepler mission that include a nearly complete set of detected planets as small as 2 R ⊕ . For each of the 156,000 target stars we assess the detectability of planets as a function of planet radius, R p , and orbital period, P , using a measure of the detection efficiency for each star. We also correct for the geometric probability of transit, R ⋆ /a. We consider first Kepler target stars within the "solar subset" having T eff = 4100-6100 K, log g = 4.0-4.9, and Kepler magnitude Kp < 15 mag, i.e. bright, main sequence GK stars. We include only those stars having photometric noise low enough to permit detection of planets down to 2 R ⊕ . We count planets in small domains of R p and P and divide by the included target stars to calculate planet occurrence in each domain. The resulting occurrence of planets varies by more than three orders of magnitude in the radius-orbital period plane and increases substantially down to the smallest radius (2 R ⊕ ) and out to the longest orbital period (50 days, ∼0.25 AU) in our study. For P < 50 days, the distribution of planet radii is given by a power law, df /d log R = k R R α with k R = 2.9 +0.5 −0.4 , α = −1.92 ± 0.11, and R = R p /R ⊕ . This rapid increase in planet occurrence with decreasing planet size agrees with the prediction of core-accretion formation, but disagrees with population synthesis models that predict a desert at super-Earth and Neptune sizes for close-in orbits. Planets with orbital periods shorter than 2 days are extremely rare; for R p > 2 R ⊕ we measure an occurrence of less than 0.001 planets per star. For all planets with orbital periods less than 50 days, we measure occurrence of 0.130 ± 0.008, 0.023 ± 0.003, and 0.013 ± 0.002 planets per star for planets with radii 2-4, 4-8, and 8-32 R ⊕ , in agreement with Doppler surveys. We fit occurrence as a function of P to a power law model with an exponential cutoff below a critical period P 0 . For smaller planets, P 0 has larger values, suggesting that the "parking distance" for migrating planets moves outward with decreasing planet size. We also measured planet occurrence over a broader stellar T eff range of 3600-7100 K, spanning M0 to F2 dwarfs. Over this range, the occurrence of 2-4 R ⊕ planets in the Kepler field linearly increases with decreasing T eff , making these small planets seven times more abundant around cool stars (3600-4100 K) than the hottest stars in our sample (6600-7100 K).

ABSTRACT. With the unprecedented photometric precision of the Kepler spacecraft, significant systematic and stochastic errors on transit signal levels are observable in the Kepler photometric data. These errors, which include discontinuities, outliers, systematic trends, and other instrumental signatures, obscure astrophysical signals. The presearch data conditioning (PDC) module of the Kepler data analysis pipeline tries to remove these errors while preserving planet transits and other astrophysically interesting signals. The completely new noise and stellar variability regime observed in Kepler data poses a significant problem to standard cotrending methods. Variable stars are often of particular astrophysical interest, so the preservation of their signals is of significant importance to the astrophysical community. We present a Bayesian maximum a posteriori (MAP) approach, where a subset of highly correlated and quiet stars is used to generate a cotrending basis vector set, which is in turn used to establish a range of "reasonable" robust fit parameters. These robust fit parameters are then used to generate a Bayesian prior and a Bayesian posterior probability distribution function (PDF) which, when maximized, finds the best fit that simultaneously removes systematic effects while reducing the signal distortion and noise injection that commonly afflicts simple least-squares (LS) fitting. A numerical and empirical approach is taken where the Bayesian prior PDFs are generated from fits to the light-curve distributions themselves.Online material: color figures AN OVERVIEW OF THE KEPLER DATA PIPELINEKepler's primary science objective is to determine the frequency of Earth-size planets transiting their Sun-like host stars in the habitable zone. 6 This daunting task demands an instrument capable of measuring the light output from each of over 100,000 stars simultaneously with an unprecedented photometric precision of 20 parts per million (ppm) at 6.5-hr intervals. The large number of stars is required because the probability of the geometrical alignment of planetary orbits that permit observation of transits is the ratio of the size of the star to the size of the planetary orbit. For Earth-like planets in 1 AU orbits about Sun-like stars, only ∼0:5% will exhibit transits. By observing such a large number of stars, Kepler is guaranteed to produce a robust result in the happy event that many Earth-size planets are detected in or near the habitable zone.The Kepler data pipeline is divided into several components in order to allow for efficient management and parallel processing of data (Jenkins 2010a). Raw pixel data downlinked from the Kepler photometer are calibrated by the calibration module (CAL) to produce calibrated target and background pixels (Quintana 2010) and their associated uncertainties (Clarke 2010). The calibrated pixels are then processed by the photometric analysis module (PA) to fit and remove sky background and extract simple aperture photometry from the backgroundcorrected, calibrated target pixel...

ABSTRACT. Kepler provides light curves of 156,000 stars with unprecedented precision. However, the raw data as they come from the spacecraft contain significant systematic and stochastic errors. These errors, which include discontinuities, systematic trends, and outliers, obscure the astrophysical signals in the light curves. To correct these errors is the task of the Presearch Data Conditioning (PDC) module of the Kepler data analysis pipeline. The original version of PDC in Kepler did not meet the extremely high performance requirements for the detection of miniscule planet transits or highly accurate analysis of stellar activity and rotation. One particular deficiency was that astrophysical features were often removed as a side effect of the removal of errors. In this article we introduce the completely new and significantly improved version of PDC which was implemented in Kepler SOC version 8.0. This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals. We describe the architecture and the algorithms of this new PDC module, show typical errors encountered in Kepler data, and illustrate the corrections using real light curve examples.

The previous presearch data conditioning algorithm, PDC-MAP, for the Kepler data processing pipeline performs very well for the majority of targets in the Kepler field of view. However, for an appreciable minority, PDC-MAP has its limitations. To further minimize the number of targets for which PDC-MAP fails to perform admirably, we have developed a new method, called multiscale MAP, or msMAP. Utilizing an overcomplete discrete wavelet transform, the new method divides each light curve into multiple channels, or bands. The light curves in each band are then corrected separately, thereby allowing for a better separation of characteristic signals and improved removal of the systematics.

We present the Kepler Object of Interest (KOI) catalog of transiting exoplanets based on searching 4 yr of Kepler time series photometry (Data Release 25, Q1-Q17). The catalog contains 8054 KOIs, of which 4034 are planet candidates with periods between 0.25and 632days. Of these candidates, 219 are new, including two in multiplanet systems (KOI-82.06 and KOI-2926.05) and 10 high-reliability, terrestrial-size, habitable zone candidates. This catalog was created using a tool called the Robovetter, which automatically vets the DR25 threshold crossing events (TCEs). The Robovetter also vetted simulated data sets and measured how well it was able to separate TCEs caused by noise from those caused by low signal-to-noise transits. We discuss the Robovetter and the metrics it uses to sort TCEs. For orbital periods less than 100 days the Robovetter completeness (the fraction of simulated transits that are determined to be planet candidates) across all observed stars is greater 1 than 85%. For the same period range, the catalog reliability (the fraction of candidates that are not due to instrumental or stellar noise) is greater than 98%. However, for low signal-to-noise candidates between 200 and 500 days around FGK-dwarf stars, the Robovetter is 76.7% complete and the catalog is 50.5% reliable. The KOI catalog, the transit fits, and all of the simulated data used to characterize this catalog are available at the NASA Exoplanet Archive.

The Kepler mission is providing photometric data of exquisite quality for the asteroseismic study of different classes of pulsating stars. These analyses place particular demands on the pre‐processing of the data, over a range of time‐scales from minutes to months. Here, we describe processing procedures developed by the Kepler Asteroseismic Science Consortium to prepare light curves that are optimized for the asteroseismic study of solar‐like oscillating stars in which outliers, jumps and drifts are corrected.