Kepler Presearch Data Conditioning I—Architecture and Algorithms for Error Correction in Kepler Light Curves

By Martin C. Stumpe, Jeffrey C. Smith, J. Cleve, Joseph D. Twicken, T. Barclay, M. Fanelli, F. Girouard, Jon M. Jenkins, J. Kolodziejczak, S. McCauliff and 1 more. 2012

590 citations recorded by the source corpus.

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.

Subfields: Astronomy and Astrophysics, Instrumentation

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Citations in the corpus

A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE K2 DATA (2015, methodology): “This basis of trends was found by running Principal Component Analysis (PCA) on a large set of (filtered) light curves and extracting the top few (∼ 4) components (Stumpe et al. 2012; Smith et al. 2012).”
TESS Eclipsing Binary Stars. I. Short-cadence Observations of 4584 Eclipsing Binaries in Sectors 1–26 (2021, methodology): “Here we chose to use the SAP light curves to avoid cases where the detrending affects the astrophysical signal (Twicken et al. 2010; Stumpe et al. 2012; Morris et al. 2020).”
PLANETARY CANDIDATES OBSERVED BY KEPLER IV: PLANET SAMPLE FROM Q1–Q8 (22 MONTHS) (2013, methodology): “In addition, the TCE population is contaminated by signals due to instrumental effects: thermal transients, pixel sensitivity dropouts, pattern noise, and video crosstalk (Caldwell et al. 2010; Kolodziejczak et al. 2010; Stumpe et al. 2012).”
TRANSIT DETECTION IN THE MEarth SURVEY OF NEARBY M DWARFS: BRIDGING THE CLEAN-FIRST, SEARCH-LATER DIVIDE (2012, methodology): “…pipeline can also disentangle instrumental systematics from stellar variability using a linear model like the ones above paired with a Maximum A Posteriori approach employing empirical priors on the decorrelation coefficients to prevent over-fitting (PDC-MAP; Smith et al. 2012; Stumpe et al. 2012).”
Rotation and differential rotation of active Kepler stars (2013, unknown): “The next reduction pipeline PDC-MAP2 (Stumpe et al. 2012; Smith et al. 2012) removes the so-called co-trending basis vectors from the data.”
Statistical properties of superflares on solar-type stars based on 1-min cadence data (2015, methodology): “We used the Presearch Data Conditioned (PDC) light curve (Stumpe et al. 2012; Smith et al. 2012)for the detection of flares.”
A STUDY OF THE SHORTEST-PERIOD PLANETS FOUND WITH KEPLER (2014, methodology): “We used the data that had been processed with the PDC-MAP algorithm (Stumpe et al. 2012; Smith et al. 2012), which is designed to remove many instrumental artifacts from the time series while preserving any astrophysical variability.”
They are small worlds after all: revised properties of Kepler M dwarf stars and their planets (2015, methodology): “Kepler Pre-search Data Conditioning (PDCSAP, Stumpe et al. 2012) removes contaminating light from nearby stars using their estimated KP magnitudes from the KIC, but this is ineffective if the stars are not resolved in the KIC (.”
Multiscale Systematic Error Correction via Wavelet-Based Bandsplitting in Kepler Data (2014, methodology): “Aside from the MAP correction being relatively expensive to perform on 11 channels individually (Stumpe et al. 2012), we found that the correction performance is better when grouping channels together this way.”
PLANETARY CANDIDATES OBSERVED BY KEPLER. VI. PLANET SAMPLE FROM Q1–Q16 (47 MONTHS) (2015, methodology): “Calibrated pixel fluxes were generated by the CAL module (Quintana et al. 2010), lightcurves were extracted by the PA module (Twicken et al. 2010), and systematic error removal was performed by the PDC module (Stumpe et al. 2012; Smith et al. 2012; Stumpe et al. 2014).”
THE APOKASC CATALOG: AN ASTEROSEISMIC AND SPECTROSCOPIC JOINT SURVEY OF TARGETS IN THE KEPLER FIELDS (2014, methodology): “We used PDC-SAP (Pre-search Data Conditioning Simple Aperture Photometry) light cuves (Smith et al. 2012; Stumpe et al. 2012) for a cohort of 2067 field stars.”
Rotation, differential rotation, and gyrochronology of active Kepler stars (2015, methodology): “This pipeline was changed to the so-called PDC-MAP (Maximum A Posteriori) pipeline (Stumpe et al. 2012; Smith et al. 2012) because the PDC pipeline coarsely removed stellar variability signals.”
Prevalence of Earth-size planets orbiting Sun-like stars (2013, background): “ But in this work, we extend our search in orbital period to P= 0.5–400 days. S5 Table S1. TERRA Grid Search Parameters P 1 P 2 Trial Transit Duration ( T) days days long cadence measurements 5.0 7.7 [3, 4, 5, 7, 9, 11] 7.7 12.0 [4, 5, 7, 9, 13] 12.0 18.6 [4, 5, 7, 9, 13, 15] 18.6 28.9 [5, 7, 9, 12,”