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 Kepler Mission, launched on Mar 6, 2009 was designed with the explicit capability to detect Earth-size planets in the habitable zone of solar-like stars using the transit photometry method. Results from just forty-three days of data along with ground-based follow-up observations have identified five new transiting planets with measurements of their masses, radii, and orbital periods. Many aspects of stellar astrophysics also benefit from the unique, precise, extended and nearly continuous data set for a large number and variety of stars. Early results for classical variables and eclipsing stars show great promise. To fully understand the methodology, processes and eventually the results from the mission, we present the underlying rationale that ultimately led to the flight and ground system designs used to achieve the exquisite photometric performance. As an example of the initial photometric results, we present variability measurements that can be used to distinguish dwarf stars from red giants.

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.