OVERVIEW OF THE KEPLER SCIENCE PROCESSING PIPELINE

By Jon M. Jenkins, Douglas A. Caldwell, H. Chandrasekaran, Joseph D. Twicken, S. Bryson, Elisa Quintana, B. Clarke, Jie Li, C. Allen, P. Tenenbaum and 20 more. 2010

332 citations recorded by the source corpus.

The Kepler Mission Science Operations Center (SOC) performs several critical functions including managing the ∼156,000 target stars, associated target tables, science data compression tables and parameters, as well as processing the raw photometric data downlinked from the spacecraft each month. The raw data are first calibrated at the pixel level to correct for bias, smear induced by a shutterless readout, and other detector and electronic effects. A background sky flux is estimated from ∼4500 pixels on each of the 84 CCD readout channels, and simple aperture photometry is performed on an optimal aperture for each star. Ancillary engineering data and diagnostic information extracted from the science data are used to remove systematic errors in the flux time series that are correlated with these data prior to searching for signatures of transiting planets with a wavelet-based, adaptive matched filter. Stars with signatures exceeding 7.1σ are subjected to a suite of statistical tests including an examination of each star's centroid motion to reject false positives caused by background eclipsing binaries. Physical parameters for each planetary candidate are fitted to the transit signature, and signatures of additional transiting planets are sought in the residual light curve. The pipeline is operational, finding planetary signatures and providing robust eliminations of false positives.

Subfields: Astronomy and Astrophysics, Electrical and Electronic Engineering, Instrumentation

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Citations in the corpus

PASTIS: Bayesian extrasolar planet validation. I. General framework, models, and performance (2014, methodology): “At this stage of the Kepler pipeline, the data have gone through a series of corrections (see Jenkins et al. 2010; Christiansen et al. 2013, and references therein), which, we assume, preserves the features of the injected signal identically.”
CHARACTERIZING THE COOL KOIs. VII. REFINED PHYSICAL PROPERTIES OF THE TRANSITING BROWN DWARF LHS 6343 C (2014, methodology): “The Kepler team defines an aperture for all targets and performs aperture photometry as a part of their Photometric Analysis (PA) pipeline, which produces a light curve from the pixel-level data (Jenkins et al. 2010).”
PRE-SPECTROSCOPIC FALSE-POSITIVE ELIMINATION OF KEPLER PLANET CANDIDATES (2010, methodology): “A discussion of the resulting flux timeseries is given in Jenkins et al. (2010b).”
Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90 (2017, methodology): “…from lists of “threshold crossing events” (TCEs; detected periodic signals that may be consistent with transiting planets) from the Kepler pipeline (Jenkins et al. 2010b,c), which were manually culled by humans to remove false positives caused by astrophysical variability and instrumental…”
Spin down of the core rotation in red giants (2012, methodology): “Original light curves were processed according to Jenkins et al. (2010) and corrected according to the pro- 85 cedure of Garćıa et al. (2011).”
Optimized transit detection algorithm to search for periodic transits of small planets (2019, methodology): “Wavelet-based algorithms (Régulo et al. 2007) are of similar detection efficiency and have been widely used for automated analyses of CoRoT (Régulo et al. 2009), Kepler (Jenkins et al. 2010), and TESS (Jenkins et al. 2016) data.”
Kepler Presearch Data Conditioning I—Architecture and Algorithms for Error Correction in Kepler Light Curves (2012, methodology): “The vast majority of these discontinuities are the result of cosmic rays or solar energetic particles striking the photometer and causing permanent local changes in pixel sensitivity, although partial exponential recovery is often observed (Jenkins et al. 2010b).”
The California-Kepler Survey. IV. Metal-rich Stars Host a Greater Diversity of Planets (2017, background): “9 Combined Differential Photometric Precision (CDPP, Jenkins et al. 2010) 14 a challenging problem which depends on how a complex, multi-stage transit pipeline performs in the face of correlated, non-stationary, and non-Gaussian photometric noise.”
INITIAL CHARACTERISTICS OF KEPLER LONG CADENCE DATA FOR DETECTING TRANSITING PLANETS (2010, methodology): “…shot noise from photon counting statistics from the direct stellar flux, sky background 2 Details of the SOC science pipeline are discussed in Jenkins et al. (2010). and shutterless readout smear, terms such as read noise and quantization noise, and the effects of processing the data, for…”
KEPLER OBSERVATIONS OF RAPID OPTICAL VARIABILITY IN ACTIVE GALACTIC NUCLEI (2011, methodology): “Kepler SAP Light Curves The Kepler standard data processing pipeline (Jenkins et al. 2010), operates on original spacecraft data to produce calibrated pixel data (Quintana et al. 2011).”
AUTOMATIC CLASSIFICATION OF KEPLER PLANETARY TRANSIT CANDIDATES (2014, methodology): “MESmin is the minimum MES discovered by the TPS, indicating repeated events above the baseline stellar flux.”
Probing the core structure and evolution of red giants using gravity-dominated mixed modes observed with Kepler (2012, methodology): “The data used are mainly Kepler long-cadence data that have been described in previous papers (e.g. 85 Jenkins et al. 2010; Bedding et al. 2010; Kallinger et al. 2010; Huber et al. 2010; Mathur et al. 2011b; Mosser et al. 2012, and references therein).”
Determining stellar macroturbulence using asteroseismic rotational velocities from Kepler (2014, methodology): “We used Kepler short-cadence observations (Gilliland et al. 2010) taken from Q5 - Q11 which were generated using simple aperture photometry (Jenkins et al. 2010) corrected for instrumental effects following the methods described by Garćıa et al. (2011).”
The TESS science processing operations center (2016, methodology): “The TESS science pipeline is being developed by the Science Processing Operations Center (SPOC) at NASA Ames Research Center based on the highly successful Kepler Science Operations Center (KSOC) pipeline.(3) Like”
PLANETARY CANDIDATES OBSERVED BY KEPLER. VI. PLANET SAMPLE FROM Q1–Q16 (47 MONTHS) (2015, methodology): “This data set was reduced and analyzed by version 9.1 of the Science Operations Center pipeline (Jenkins et al. 2010).”