53 research outputs found
Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves
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 to removal of errors. In this
paper we introduce the completely new and significantly improved version of PDC
which was implemented in Kepler SOC 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.Comment: Submitted to PASP. Also see companion paper "Kepler Presearch Data
Conditioning II - A Bayesian Approach to Systematic Error Correction" by Jeff
C. Smith et a
Transiting Planet Search in the Kepler Pipeline
The Kepler Mission simultaneously measures the brightness of more than 160,000 stars every 29.4 minutes over a 3.5-year mission to search for transiting planets. Detecting transits is a signal-detection problem where the signal of interest is a periodic pulse train and the predominant noise source is non-white, non-stationary (1/f) type process of stellar variability. Many stars also exhibit coherent or quasi-coherent oscillations. The detection algorithm first identifies and removes strong oscillations followed by an adaptive, wavelet-based matched filter. We discuss how we obtain super-resolution detection statistics and the effectiveness of the algorithm for Kepler flight data
Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction
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 such as SYSREM and TFA.
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 which 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.Comment: 43 pages, 21 figures, Submitted for publication in PASP. Also see
companion paper "Kepler Presearch Data Conditioning I - Architecture and
Algorithms for Error Correction in Kepler Light Curves" by Martin C. Stumpe,
et a
Pixel-Level Calibration in the Kepler Science Operations Center Pipeline
We present an overview of the pixel-level calibration of flight data from the Kepler Mission performed within the Kepler Science Operations Center Science Processing Pipeline. This article describes the calibration (CAL) module, which operates on original spacecraft data to remove instrument effects and other artifacts that pollute the data. Traditional CCD data reduction is performed (removal of instrument/detector effects such as bias and dark current), in addition to pixel-level calibration (correcting for cosmic rays and variations in pixel sensitivity), Kepler-specific corrections (removing smear signals which result from the lack of a shutter on the photometer and correcting for distortions induced by the readout electronics), and additional operations that are needed due to the complexity and large volume of flight data. CAL operates on long (~30 min) and short (~1 min) sampled data, as well as full-frame images, and produces calibrated pixel flux time series, uncertainties, and other metrics that are used in subsequent Pipeline modules. The raw and calibrated data are also archived in the Multi-mission Archive at Space Telescope at the Space Telescope Science Institute for use by the astronomical community
Data Validation in the Kepler Science Operations Center Pipeline
We present an overview of the Data Validation (DV) software component and its context within the Kepler Science Operations Center (SOC) pipeline and overall Kepler Science mission. The SOC pipeline performs a transiting planet search on the corrected light curves for over 150,000 targets across the focal plane array. We discuss the DV strategy for automated validation of Threshold Crossing Events (TCEs) generated in the transiting planet search. For each TCE, a transiting planet model is fitted to the target light curve. A multiple planet search is conducted by repeating the transiting planet search on the residual light curve after the model flux has been removed; if an additional detection occurs, a planet model is fitted to the new TCE. A suite of automated tests are performed after all planet candidates have been identified. We describe a centroid motion test to determine the significance of the motion of the target photocenter during transit and to estimate the coordinates of the transit source within the photometric aperture; a series of eclipsing binary discrimination tests on the parameters of the planet model fits to all transits and the sequences of odd and even transits; and a statistical bootstrap to assess the likelihood that the TCE would have been generated purely by chance given the target light curve with all transits removed. Keywords: photometry, data validation, Kepler, Earth-size planet
Detection of Potential Transit Signals in Sixteen Quarters of Kepler Mission Data
We present the results of a search for potential transit signals in four
years of photometry data acquired by the Kepler Mission. The targets of the
search include 111,800 stars which were observed for the entire interval and
85,522 stars which were observed for a subset of the interval. We found that
9,743 targets contained at least one signal consistent with the signature of a
transiting or eclipsing object, where the criteria for detection are
periodicity of the detected transits, adequate signal-to-noise ratio, and
acceptance by a number of tests which reject false positive detections. When
targets that had produced a signal were searched repeatedly, an additional
6,542 signals were detected on 3,223 target stars, for a total of 16,285
potential detections. Comparison of the set of detected signals with a set of
known and vetted transit events in the Kepler field of view shows that the
recovery rate for these signals is 96.9%. The ensemble properties of the
detected signals are reviewed.Comment: Accepted by ApJ Supplemen
A Framework for Propagation of Uncertainties in the Kepler Data Analysis Pipeline
The Kepler space telescope is designed to detect Earth-like planets around Sun-like stars using transit photometry by simultaneously observing 100,000 stellar targets nearly continuously over a three and a half year period. The 96-megapixel focal plane consists of 42 charge-coupled devices (CCD) each containing two 1024 x 1100 pixel arrays. Cross-correlations between calibrated pixels are introduced by common calibrations performed on each CCD requiring downstream data products access to the calibrated pixel covariance matrix in order to properly estimate uncertainties. The prohibitively large covariance matrices corresponding to the ~75,000 calibrated pixels per CCD preclude calculating and storing the covariance in standard lock-step fashion. We present a novel framework used to implement standard propagation of uncertainties (POU) in the Kepler Science Operations Center (SOC) data processing pipeline. The POU framework captures the variance of the raw pixel data and the kernel of each subsequent calibration transformation allowing the full covariance matrix of any subset of calibrated pixels to be recalled on-the-fly at any step in the calibration process. Singular value decomposition (SVD) is used to compress and low-pass filter the raw uncertainty data as well as any data dependent kernels. The combination of POU framework and SVD compression provide downstream consumers of the calibrated pixel data access to the full covariance matrix of any subset of the calibrated pixels traceable to pixel level measurement uncertainties without having to store, retrieve and operate on prohibitively large covariance matrices. We describe the POU Framework and SVD compression scheme and its implementation in the Kepler SOC pipeline
- …