Miles T. Cote

OVERVIEW OF THE KEPLER SCIENCE PROCESSING PIPELINE

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 7.1? are subjected to a suite of statistical tests including an examination of each stars 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.

KOI-126: A Triply Eclipsing Hierarchical Triple with Two Low-Mass Stars

The Kepler telescope detected a triple stellar system and reveals details of the structure of low-mass stars. The Kepler spacecraft has been monitoring the light from 150,000 stars in its primary quest to detect transiting exoplanets. Here, we report on the detection of an eclipsing stellar hierarchical triple, identified in the Kepler photometry. KOI-126 [A, (B, C)], is composed of a low-mass binary [masses MB = 0.2413 ± 0.0030 solar mass (M☉), MC = 0.2127 ± 0.0026 M☉; radii RB = 0.2543 ± 0.0014 solar radius (R☉), RC = 0.2318 ± 0.0013 R☉; orbital period P1 = 1.76713 ± 0.00019 days] on an eccentric orbit about a third star (mass MA = 1.347 ± 0.032 M☉; radius RA = 2.0254 ± 0.0098 R☉; period of orbit around the low-mass binary P2 = 33.9214 ± 0.0013 days; eccentricity of that orbit e2 = 0.3043 ± 0.0024). The low-mass pair probe the poorly sampled fully convective stellar domain offering a crucial benchmark for theoretical stellar models.

Discovery and Rossiter-McLaughlin Effect of Exoplanet Kepler-8b

We report on the discovery and the Rossiter-McLaughlin (R-M) effect of Kepler-8b, a transiting planet identified by the NASA Kepler Mission. Kepler photometry and Keck-HIRES radial velocities yield the radius and mass of the planet around this F8IV subgiant host star. The planet has a radius R_P = 1.419 R_J and a mass M_P = 0.60 M_J, yielding a density of 0.26 g cm^(–3), one of the lowest planetary densities known. The orbital period is P = 3.523 days and the orbital semimajor axis is 0.0483^(+0.0006) _(–0.0012) AU. The star has a large rotational vsin i of 10.5 ± 0.7 km s^(–1) and is relatively faint (V ≈ 13.89 mag); both properties are deleterious to precise Doppler measurements. The velocities are indeed noisy, with scatter of 30 m s^(–1), but exhibit a period and phase that are consistent with those implied by transit photometry. We securely detect the R-M effect, confirming the planets existence and establishing its orbit as prograde. We measure an inclination between the projected planetary orbital axis and the projected stellar rotation axis of λ = –26o.4 ± 10o.1, indicating a significant inclination of the planetary orbit. R-M measurements of a large sample of transiting planets from Kepler will provide a statistically robust measure of the true distribution of spin-orbit orientations for hot Jupiters around F and early G stars.

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.

DETECTION OF POTENTIAL TRANSIT SIGNALS IN THE FIRST THREE QUARTERS OF Kepler MISSION DATA

We present the results of a search for potential transit signals in the first three quarters of photometry data acquired by the Kepler mission. The targets of the search include 151,722 stars which were observed over the full interval and an additional 19,132 stars which were observed for only one or two quarters. From this set of targets we find a total of 5392 detections which meet the Kepler detection criteria: those criteria are periodicity of signal, an acceptable signal-to-noise ratio, and a composition test which rejects spurious detections which contain non-physical combinations of events. The detected signals are dominated by events with relatively low signal-to-noise ratio and by events with relatively short periods. The distribution of estimated transit depths appears to peak in the range between 40 and 100 parts per million, with a few detections down to fewer than 10 parts per million. The detections exhibit signal-to-noise ratios from 7.1σ, which is the lower cutoff for detections, to over 10,000σ, and periods ranging from 0.5 days, which is the lower cutoff used in the procedure, to 109 days, which is the upper limit of achievable periods given the length of the data set and the criteria used for detections. The detected signals are compared to a set of known transit events in the Kepler field of view which were derived by a different method using a longer data interval; the comparison shows that the current search correctly identified 88.1% of the known events. A tabulation of the detected transit signals, examples which illustrate the analysis and detection process, a discussion of future plans and open, potentially fruitful, areas of further research are included.

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.

AUTOMATIC CLASSIFICATION OF KEPLER PLANETARY TRANSIT CANDIDATES

In the first three years of operation, the Kepler mission found 3697 planet candidates (PCs) from a set of 18,406 transit-like features detected on more than 200,000 distinct stars. Vetting candidate signals manually by inspecting light curves and other diagnostic information is a labor intensive effort. Additionally, this classification methodology does not yield any information about the quality of PCs; all candidates are as credible as any other. The torrent of exoplanet discoveries will continue after Kepler, because a number of exoplanet surveys will have an even broader search area. This paper presents the application of machine-learning techniques to the classification of the exoplanet transit-like signals present in the Kepler light curve data. Transit-like detections are transformed into a uniform set of real-numbered attributes, the most important of which are described in this paper. Each of the known transit-like detections is assigned a class of PC; astrophysical false positive; or systematic, instrumental noise. We use a random forest algorithm to learn the mapping from attributes to classes on this training set. The random forest algorithm has been used previously to classify variable stars; this is the first time it has been used for exoplanet classification. We are able to achieve an overall error rate of 5.85% and an error rate for classifying exoplanets candidates of 2.81%.

Selecting pixels for Kepler downlink

The Kepler mission monitors ~ 165, 000 stellar targets using 42 2200 × 1024 pixel CCDs. Onboard storage and bandwidth constraints prevent the storage and downlink of all 96 million pixels per 30-minute cadence, so the Kepler spacecraft downlinks a specified collection of pixels for each target. These pixels are selected by considering the object brightness, background and the signal-to-noise in each pixel, and maximizing the signal-to- noise ratio of the target. This paper describes pixel selection, creation of spacecraft apertures that efficiently capture selected pixels, and aperture assignment to a target. Engineering apertures, short-cadence targets and custom-specified shapes are discussed.

Kepler Science Operations Center architecture

We give an overview of the operational concepts and architecture of the Kepler Science Processing Pipeline. Designed, developed, operated, and maintained by the Kepler Science Operations Center (SOC) at NASA Ames Research Center, the Science Processing Pipeline is a central element of the Kepler Ground Data System. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center which hosts the computers required to perform data analysis. The SOCs charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Processing Pipeline, including the hardware infrastructure, scientific algorithms, and operational procedures. We present the high-performance, parallel computing software modules of the pipeline that perform transit photometry, pixel-level calibration, systematic error correction, attitude determination, stellar target management, and instrument characterization. We show how data processing environments are divided to support operational processing and test needs. We explain the operational timelines for data processing and the data constructs that flow into the Kepler Science Processing Pipeline.