Engineering topics
Publications and source records attributed to Burke, Christopher J..
This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 5 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics.
This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 6 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics.
We have adapted the algorithmic tools developed during the Kepler mission to vet the quality of transit-like signals for use on the K2 mission data. Using the four sets of publicly available light curves at MAST, we produced a uniformly vetted catalog of 772 transiting planet candidates from K2 as listed at the NASA Exoplanet Archive in the K2 Table of Candidates. Our analysis marks 676 of these as planet candidates and 96 as false positives. All confirmed planets pass our vetting tests. Sixty of our false positives are new identifications, effectively doubling the overall number of astrophysical signals mimicking planetary transits in K2 data. Most of the targets listed as false positives in our catalog show either prominent secondary eclipses, transit depths suggesting a stellar companion instead of a planet, or significant photocenter shifts during transit. We packaged our tools into the open-source, automated vetting pipeline Discovery and Vetting of Exoplanets (DAVE), designed to streamline follow-up efforts by reducing the time and resources wasted observing targets that are likely false positives. DAVE will also be a valuable tool for analyzing planet candidates from NASA's TESS mission, where several guest-investigator programs will provide independent light-curve sets—and likely many more from the community. We are currently testing DAVE on recently released TESS planet candidates and will present our results in a follow-up paper.
This release note discusses the science data products produced by the Science Processing Operations Center at Ames Research Center from Sector 4 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics.
This release note discusses the planetary transit search data products produced by the Science Processing Operations Center at Ames Research Center from Sectors 1-2 observations made with the TESS (Transiting Exoplanet Survey Satellite) spacecraft and cameras as a means to document instrument performance and data characteristics.
This release note discusses the planetary transit search data products produced by the Science Processing Operations Center at Ames Research Center from Sectors 1-3 observations made with the TESS spacecraft and cameras as a means to document instrument performance and data characteristics.
This paper discusses the transit model-fitting and multiple-planet search algorithms and performance of the Kepler Science Data Processing Pipeline, developed by the Kepler Science Operations Center (SOC). Threshold crossing events (TCEs), which are transit candidate events, are generated by the Transiting Planet Search (TPS) component of the pipeline and subsequently processed in the data validation (DV) component. The transit model is used in DV to fit TCEs to characterize planetary candidates and to derive parameters that are used in various diagnostic tests to classify them. After the signature associated with the TCE is removed from the light curve of the target star, the residual light curve goes through TPS again to search for additional TCEs. The iterative process of transit model fitting and multiple-planet search continues until no TCE is generated from the residual light curve or an upper limit is reached. The transit model-fitting and multiple-planet search performance of the final release (9.3, 2016January) of the pipeline is demonstrated with the results of the processing of four years (17 quarters) of flight data from the primary Kepler Mission. The transit model-fitting results are accessible from the NASA Exoplanet Archive. The final version of the SOC codebase is available through GitHub.
Quantifying the ability of a transiting planet survey to recover transit signals has commonly been accomplished through Monte-Carlo injection of transit signals into the observed data and subsequent running of the signal search algorithm (Gilliland et al., 2000; Weldrake et al., 2005; Burke et al., 2006). In order to characterize the performance of the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017) on a sample of over 200,000 stars, two complementary injection and recovery tests are utilized:1. Injection of a single transit signal per target into the image or pixel-level data, hereafter referred to as pixel-level transit injection (PLTI), with subsequent processing through the Photometric Analysis (PA), Presearch Data Conditioning (PDC), Transiting Planet Search (TPS), and Data Validation (DV) modules of the Kepler pipeline. The PLTI quantification of the Kepler pipeline's completeness has been described previously by Christiansen et al. (2015, 2016); the completeness of the final SOC 9.3 Kepler pipeline acting on the Data Release 25 (DR25) light curves is described by Christiansen (2017).2. Injection of multiple transit signals per target into the normalized flux time series data with a subsequent transit search using a stream-lined version of the Transiting Planet Search (TPS) module. This test, hereafter referred to as flux-level transit injection (FLTI), is the subject of this document. By running a heavily modified version of TPS, FLTI is able to perform many injections on selected targets and determine in some detail which injected signals are recoverable. Significant numerical efficiency gains are enabled by precomputing the data conditioning steps at the onset of TPS and limiting the search parameter space (i.e., orbital period, transit duration, and ephemeris zero-point) to a small region around each injected transit signal.The PLTI test has the advantage that it follows transit signals through all processing steps of the Kepler pipeline, and the recovered signals can be further classified as planet candidates or false positives in the exact same manner as detections from the nominal (i.e., observed) pipeline run (Twicken et al., 2016, Thompson et al., in preparation). To date, the PLTI test has been the standard means of measuring pipeline completeness averaged over large samples of targets (Christiansen et al., 2015, 2016; Christiansen, 2017). However, since the PLTI test uses only one injection per target, it does not elucidate individual-target variations in pipeline completeness due to differences in stellar properties or astrophysical variability. Thus, we developed the FLTI test to provide a numerically efficient way to fully map individual targets and explore the performance of the pipeline in greater detail. The FLTI tests thereby allow a thorough validation of the pipeline completeness models (such as window function (Burke and Catanzarite, 2017a), detection efficiency (Burke Catanzarite, 2017b), etc.) across the spectrum of Kepler targets (i.e., various astrophysical phenomena and differences in instrumental noise). Tests during development of the FLTI capability revealed that there are significant target-to-target variations in the detection efficiency.
A necessary input to planet occurrence calculations is an accurate model for the pipeline completeness (Burke et al., 2015). This document describes the use of the Kepler planet occurrence rate products in order to calculate a per-target detection contour for the measured Data Release 25 (DR25) pipeline performance. A per-target detection contour measures for a given combination of orbital period, Porb, and planet radius, Rp, what fraction of transit signals are recoverable by the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017). The steps for calculating a detection contour follow the procedure outlined in Burke et al. (2015), but have been updated to provide improved accuracy enabled by the substantially larger database of transit injection and recovery tests that were performed on the final version (i.e., SOC 9.3) of the Kepler pipeline (Christiansen, 2017; Burke Catanzarite, 2017a). In the following sections, we describe the main inputs to the per-target detection contour and provide a worked example of the python software released with this document (Kepler Planet Occurrence Rate Tools KeplerPORTs)1 that illustrates the generation of a detection contour in practice. As background material for this document and its nomenclature, we recommend the reader be familiar with the previous method of calculating a detection contour (Section 2 of Burke et al.,2015), input parameters relevant for describing the data quantity and quality of Kepler targets (Burke Catanzarite, 2017b), and the extensive new transit injection and recovery tests of the Kepler pipeline (Christiansen et al., 2016; Burke Catanzarite, 2017a; Christiansen, 2017).
This document describes the window and one-sigma depth functions relevant to the Transiting Planet Search (TPS) algorithm in the Kepler pipeline (Jenkins 2002; Jenkins et al. 2017). The window function specifies the fraction of unique orbital ephemeris epochs over which three transits are observable as a function of orbital period. In this context, the epoch and orbital period, together, comprise the ephemeris of an orbiting companion, and ephemerides with the same period are considered equivalent if their epochs differ by an integer multiple of the period. The one-sigma depth function specifies the depth of a signal (in ppm) for a given light curve that results in a one-sigma detection of a transit signature as a function of orbital period when averaged over all unique orbital ephemerides. These planet detection metrics quantify the ability of TPS to detect a transiting planet signature on a star-by-star basis. They are uniquely applicable to a specific Kepler data release, since they are dependent on the details of the light curves searched and the functionality of the TPS algorithm used to perform the search. This document describes the window and one-sigma depth functions relevant to Kepler Data Release 25 (DR25), where the data were processed (Thompson et al. 2016) and searched (Twicken et al. 2016) with the SOC 9.3 pipeline. In Section 4, we describe significant differences from those reported in Kepler Data Release 24 (Burke Seader 2016) and document our verification method.
This document describes the data produced by the Statistical Bootstrap Test over the final three Threshold Crossing Event (TCE) deliveries to NExScI: SOC 9.1 (Q1Q16)1 (Tenenbaum et al. 2014), SOC 9.2 (Q1Q17) aka DR242 (Seader et al. 2015), and SOC 9.3 (Q1Q17) aka DR253 (Twicken et al. 2016). The last few years have seen significant improvements in the SOC science data processing pipeline, leading to higher quality light curves and more sensitive transit searches. The statistical bootstrap analysis results presented here and the numerical results archived at NASAs Exoplanet Science Institute (NExScI) bear witness to these software improvements. This document attempts to introduce and describe the main features and differences between these three data sets as a consequence of the software changes.
We present our analysis of archival Hubble Space Telescope Wide Field Planetary Camera 2 (WFPC2) observations in F555W (approximately V) and F814W (approximately I) of the central region of the Ursa Minor dwarf spheroidal galaxy. The V versus V - I color-magnitude diagram features a sparsely populated blue horizontal branch, a steep thin red giant branch, and a narrow subgiant branch. The main sequence reaches approximately 2 magnitudes below the main-sequence turnoff (V(sup UMi, sub TO) approximately equals 23.27 +/- 0.11 mag) of the median stellar population. We compare the fiducial sequence of the Galactic globular cluster M92 (NGC 6341). The excellent match between Ursa Minor and M92 confirms that the median stellar population of the UMi dSph galaxy is metal poor ([Fe/H](sub UMi) approximately equals [Fe/H](sub M92) approximately equals -2.2 dex) and ancient (age(sub UMi)approximately equalsage(sub M92) approximately equals 14 Gyr). The B - V reddening and the absorption in V are estimated to be E(B - V) = 0.03 +/- 0.01 mag and A(sup UMi, sub V) = 0.09 +/- 0.03 mag. A new estimate of the distance modulus of Ursa Minor, (m - M)(sup UMi, sub 0) = 19.18 +/- 0.12 mag, has been derived based on fiducial-sequence fitting M92 [DELTA.V(sub UMi - M92) = 4.60 +/- 0.03 mag and DELTA(V - I)(sub UMi - M92) = 0.010 +/- 0.005 mag] and the adoption of the apparent V distance modulus for M92 of (m - M)(sup M92, sub V) = 14.67 +/- 0.08 mag (Pont et al. 1998, A&A, 329, 87). The Ursa Minor dwarf spheroidal galaxy is then at a distance of 69 +/- 4 kpc from the Sun. These HST observations indicate that Ursa Minor has had a very simple star formation history consisting mainly of a single major burst of star formation about 14 Gyr ago which lasted approximately < 2 Gyr. While we may have missed minor younger stellar populations due to the small field-of-view of the WFPC2 instrument, these observations clearly show that most of the stars in the central region Ursa Minor dwarf spheroidal galaxy are ancient. If the ancient Galactic globular clusters, like M92, formed concurrently with the early formation of the Milky Way galaxy itself, then the Ursa Minor dwarf spheroidal is probably as old as the Milky Way.