Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction

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

684 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Applied Mathematics, Signal Processing

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Citations in the corpus

A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE K2 DATA (2015, methodology): “…on the ideas behind previous data-driven de-trending procedures such as the Kepler pipeline pre-search data conditioning (PDC ; Stumpe et al. 2012; Smith et al. 2012), but (because of our simultaneous fitting approach) we can use a much more flexible systematics model without worrying about…”
Measuring the rotation period distribution of field M dwarfs with Kepler (2013, methodology): “In contrast with earlier versions, the new PDC-MAP uses a Bayesian approach to do so while retaining most real (astrophysical) variability (Smith et al. 2012; Stumpe et al. 2012).”
The California-Kepler Survey. I. High-resolution Spectroscopy of 1305 Stars Hosting Kepler Transiting Planets (2017, methodology): “…the software engineering that transformed CCD pixel values into planet candidates (Jenkins et al. 2010; Gilliland et al. 2010; Stumpe et al. 2012; Smith et al. 2012, 2016; Batalha et al. 2010a,b; Torres et al. 2011; Bryson et al. 2013; Christiansen et al. 2012, 2013, 2015, 2016; Thompson et al.…”
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.”
Precise time series photometry for the Kepler-2.0 mission (2014, methodology): “The systematics correction in the PDC-MAP pipeline used during the nominal Kepler mission (Smith et al. 2012) is a variant on this approach, where a Bayesian framework is used to ensure that the coefficients relating each star’s light curve to each trend are similar for stars of similar magnitude…”
KEPLER FLARES. I. ACTIVE AND INACTIVE M DWARFS (2014, methodology): “We used the most recent publicly available version of the Kepler photometry, which gives the Pre-search Data Conditioning Simple Aperture Photometry (PDCSAP) flux (Smith et al. 2012).”
White Dwarf Rotation as a Function of Mass and a Dichotomy of Mode Line Widths: Kepler Observations of 27 Pulsating DA White Dwarfs through K2 Campaign 8 (2017, methodology): “For the five DAVs observed in the original Kepler mission, we analyzed the light curves processed by the GO office (Smith et al. 2012; Stumpe et al. 2012).”
The connection between stellar granulation and oscillation as seen by the Kepler mission (2014, background): “Even though these instrumental effects can be corrected to some extent (see, e.g., García et al. 2011; Smith et al. 2012, and references therein), they prevent us from accessing intrinsic variability timescales longer than a few weeks.”
ROTATION PERIODS OF 34,030 KEPLER MAIN-SEQUENCE STARS: THE FULL AUTOCORRELATION SAMPLE (2014, methodology): “We used the data corrected for instrumental systematics using PDC-MAP (Smith et al. 2012; Stumpe et al. 2012), which removes the majority of instrumental glitches and systematic trends using a Bayesian approach, while retaining most real variability.”
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).”
Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90 (2017, methodology): “…calibrates the pixels, identifies optimal photometric apertures, performs simple (stationary) aperture photometry, and removes common-mode instrumental artifacts using the Presearch Data Conditioning–Maximum A Posteriori (PDCMAP) method (Stumpe et al. 2012; Smith et al. 2012; Stumpe et al. 2014).”
THE KEPLER-10 PLANETARY SYSTEM REVISITED BY HARPS-N: A HOT ROCKY WORLD AND A SOLID NEPTUNE-MASS PLANET (2014, background): “Excluding quarters 4 and 17 because they were not detrended by the PDCMAP reduction pipeline (Smith et al. 2012), and quarter 9 because of instrumental systematics, the maximum peak-to-peak variation in the photometry is less than 500 ppm. Assuming that this variation is due to activity and not to…”
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, neutral): “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.”
AUTOMATIC CLASSIFICATION OF KEPLER PLANETARY TRANSIT CANDIDATES (2014, methodology): “After calibration, light curves are produced using simple aperture photometry and subjected to cotrending to remove systematic noise (Smith et al. 2012; Stumpe et al. 2012).”
A PLATEAU IN THE PLANET POPULATION BELOW TWICE THE SIZE OF EARTH (2013, neutral): “We describe our selection of 12,000 low-noise targets the Kepler team planet search pipeline has continued to evolve (Smith et al. 2012; Stumpe et al. 2012).”