Engineering topics

Jenkins, Jon M.

Publications and source records attributed to Jenkins, Jon M..

At least 73 records · Page 4

NASA NTRS2016

Kepler Data Release 25 Notes (Q0-Q17)

These Data Release Notes provide information specific to the current reprocessing and re-export of the Q0-Q17 data. The data products included in this data release include target pixel files, light curve files, FFIs,CBVs, ARP, Background, and Collateral files. This release marks the final processing of the Kepler Mission Data. See Tables 1 and 2 for a list of the reprocessed Kepler cadence data. See Table 3 for a list of the available FFIs. The Long Cadence Data, Short Cadence Data, and FFI data are documented in these data release notes. The ancillary files (i.e., cotrending basis vectors, artifact removal pixels, background, and collateral data) are described in the Archive Manual (Thompson et al., 2016).

Data Release Notes↗

NASA NTRS2016

Kepler Planet Detection Metrics: Statistical Bootstrap Test

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.

Bootstrap↗

NASA NTRS2015

Supercomputing in the Age of Discovering Superearths, Earths and Exoplanet Systems

NASA's Kepler Mission was launched in March 2009 as NASA's first mission capable of finding Earth-size planets orbiting in the habitable zone of Sun-like stars, that range of distances for which liquid water would pool on the surface of a rocky planet. Kepler has discovered over 1000 planets and over 4600 candidates, many of them as small as the Earth. Today, Kepler's amazing success seems to be a fait accompli to those unfamiliar with her history. But twenty years ago, there were no planets known outside our solar system, and few people believed it was possible to detect tiny Earth-size planets orbiting other stars. Motivating NASA to select Kepler for launch required a confluence of the right detector technology, advances in signal processing and algorithms, and the power of supercomputing.

exoplanet↗

NASA NTRS2015

Supercomputing in the Age of Discovering Superearths, Earths and Exoplanet Systems

With the Kepler Mission, exoplanet science has entered the era of Big Data and has benefited tremendously from the availability of the NAS (NASA Advanced Supercomputing Division) Pleiades supercomputer. This presentation provides an overview of the history of exoplanet discoveries and highlights the key enabling technologies for Kepler. I provide an overview of the Kepler science pipeline and the algorithms used to analyze the data and detect transiting planets, all of which run routinely on the NAS Pleiades system. The presentation also provides a description of the Blender analysis program used to validate Kepler planet discoveries statistically, and the effort to discover exomoons lurking in Kepler data, both of which also run on Pleiades. Finally, I describe NASA's TESS (Transiting Exoplanet Survey Satellite) Mission, which leverages Kepler heritage to provide a science pipeline similar to that for Kepler and that will also run routinely on the NAS Pleiades supercomputer.

big data↗

NASA NTRS2015

From Pixels to Planets

The Kepler Mission was launched in 2009 as NASAs first mission capable of finding Earth-size planets in the habitable zone of Sun-like stars. Its telescope consists of a 1.5-m primary mirror and a 0.95-m aperture. The 42 charge-coupled devices in its focal plane are read out every half hour, compressed, and then downlinked monthly. After four years, the second of four reaction wheels failed, ending the original mission. Back on earth, the Science Operations Center developed the Science Pipeline to analyze about 200,000 target stars in Keplers field of view, looking for evidence of periodic dimming suggesting that one or more planets had crossed the face of its host star. The Pipeline comprises several steps, from pixel-level calibration, through noise and artifact removal, to detection of transit-like signals and the construction of a suite of diagnostic tests to guard against false positives. The Kepler Science Pipeline consists of a pipeline infrastructure written in the Java programming language, which marshals data input to and output from MATLAB applications that are executed as external processes. The pipeline modules, which underwent continuous development and refinement even after data started arriving, employ several analytic techniques, many developed for the Kepler Project. Because of the large number of targets, the large amount of data per target and the complexity of the pipeline algorithms, the processing demands are daunting. Some pipeline modules require days to weeks to process all of their targets, even when run on NASA's 128-node Pleiades supercomputer. The software developers are still seeking ways to increase the throughput. To date, the Kepler project has discovered more than 4000 planetary candidates, of which more than 1000 have been independently confirmed or validated to be exoplanets. Funding for this mission is provided by NASAs Science Mission Directorate.

supercomputers↗

NASA NTRS2013

Improving Photometry and Stellar Signal Preservation with Pixel-Level Systematic Error Correction

The Kepler Mission has demonstrated that excellent stellar photometric performance can be achieved using apertures constructed from optimally selected CCD pixels. The clever methods used to correct for systematic errors, while very successful, still have some limitations in their ability to extract long-term trends in stellar flux. They also leave poorly correlated bias sources, such as drifting moiré pattern, uncorrected. We will illustrate several approaches where applying systematic error correction algorithms to the pixel time series, rather than the co-added raw flux time series, provide significant advantages. Examples include, spatially localized determination of time varying moiré pattern biases, greater sensitivity to radiation-induced pixel sensitivity drops (SPSDs), improved precision of co-trending basis vectors (CBV), and a means of distinguishing the stellar variability from co-trending terms even when they are correlated. For the last item, the approach enables physical interpretation of appropriately scaled coefficients derived in the fit of pixel time series to the CBV as linear combinations of various spatial derivatives of the pixel response function (PRF). We demonstrate that the residuals of a fit of soderived pixel coefficients to various PRF-related components can be deterministically interpreted in terms of physically meaningful quantities, such as the component of the stellar flux time series which is correlated with the CBV, as well as, relative pixel gain, proper motion and parallax. The approach also enables us to parameterize and assess the limiting factors in the uncertainties in these quantities.

Kolodzijczak, Jeffrey J.↗

NASA NTRS2011

Modeling Kepler Transit Light Curves as False Positives: Rejection of Blend Scenarios for Kepler-9, and Validation of Kepler-9 d, a Super-Earth-Size Planet in a Multiple System

Light curves from the Kepler Mission contain valuable information on the nature of the phenomena producing the transit-like signals. To assist in exploring the possibility that they are due to an astrophysical false positive we describe a procedure (BLENDER) to model the photometry in terms of a blend rather than a planet orbiting a star. A blend may consist of a background or foreground eclipsing binary (or star-planet pair) whose eclipses are attenuated by the light of the candidate and possibly other stars within the photometric aperture. We apply BLENDER to the case of Kepler-9 (KIC 3323887), a target harboring two previously confirmed Saturn-size planets (Kepler-9 b and Kepler-9 c) showing transit timing variations, and an additional shallower signal with a 1.59 day period suggesting the presence of a super-Earth-size planet. Using BLENDER together with constraints from other follow-up observations we are able to rule out all blends for the two deeper signals and provide independent validation of their planetary nature. For the shallower signal, we rule out a large fraction of the false positives that might mimic the transits. The false alarm rate for remaining blends depends in part (and inversely) on the unknown frequency of small-size planets. Based on several realistic estimates of this frequency, we conclude with very high confidence that this small signal is due to a super-Earth-size planet (Kepler-9 d) in a multiple system, rather than a false positive. The radius is determined to be 1.64(exp)(sub-14),R, and current spectroscopic observations are as yet insufficient to establish its mass.

PLANETARY SYSTEMS↗

NASA NTRS2010

Flagging and Correction of Pattern Noise in the Kepler Focal Plane Array

In order for Kepler to achieve its required less than 20 PPM photometric precision for magnitude 12 and brighter stars, instrument-induced variations in the CCD readout bias pattern (our "2D black image"), which are either fixed or slowly varying in time, must be identified and the corresponding pixels either corrected or removed from further data processing. The two principle sources of these readout bias variations are crosstalk between the 84 science CCDs and the 4 fine guidance sensor (FGS) CCDs and a high frequency amplifier oscillation on less than 40% of the CCD readout channels. The crosstalk produces a synchronous pattern in the 2D black image with time-variation observed in less than 10% of individual pixel bias histories. We will describe a method of removing the crosstalk signal using continuously-collected data from masked and over-clocked image regions (our "collateral data"), and occasionally-collected full-frame images and reverse-clocked readout signals. We use this same set to detect regions affected by the oscillating amplifiers. The oscillations manifest as time-varying moir pattern and rolling bands in the affected channels. Because this effect reduces the performance in only a small fraction of the array at any given time, we have developed an approach for flagging suspect data. The flags will provide the necessary means to resolve any potential ambiguity between instrument-induced variations and real photometric variations in a target time series. We will also evaluate the effectiveness of these techniques using flight data from background and selected target pixels.

Kolodziejczak, Jeffery J.↗

NASA NTRS2010

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.

Jenkins, Jon M.↗

NASA NTRS2010

The Kepler End-to-End Model: Creating High-Fidelity Simulations to Test Kepler Ground Processing

The Kepler mission is designed to detect the transit of Earth-like planets around Sun-like stars by observing 100,000 stellar targets. Developing and testing the Kepler ground-segment processing system, in particular the data analysis pipeline, requires high-fidelity simulated data. This simulated data is provided by the Kepler End-to-End Model (ETEM). ETEM simulates the astrophysics of planetary transits and other phenomena, properties of the Kepler spacecraft and the format of the downlinked data. Major challenges addressed by ETEM include the rapid production of large amounts of simulated data, extensibility and maintainability.

Bryson, Stephen T.↗

NASA NTRS2010

Selecting Pixels for Kepler Downlink

The Kepler mission monitors > 100,000 stellar targets using 42 2200 1024 pixel CCDs. Bandwidth constraints prevent the 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 of each pixel, and are optimized to maximize 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. Diagnostic apertures, short-cadence targets and custom specified shapes are discussed.

Bryson, Stephen T.↗

NASA NTRS2010

The Kepler Science Operations Center Pipeline Framework Extensions

The Kepler Science Operations Center (SOC) is responsible for several aspects of the Kepler Mission, including managing targets, generating on-board data compression tables, monitoring photometer health and status, processing the science data, and exporting the pipeline products to the mission archive. We describe how the generic pipeline framework software developed for Kepler is extended to achieve these goals, including pipeline configurations for processing science data and other support roles, and custom unit of work generators that control how the Kepler data are partitioned and distributed across the computing cluster. We describe the interface between the Java software that manages the retrieval and storage of the data for a given unit of work and the MATLAB algorithms that process these data. The data for each unit of work are packaged into a single file that contains everything needed by the science algorithms, allowing these files to be used to debug and evolve the algorithms offline.

Klaus, Todd C.↗

NASA NTRS2010

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.

Clarke, Bruce D.↗

NASA NTRS2010

Photometer Performance Assessment in Kepler Science Data Processing

This paper describes the algorithms of the Photometer Performance Assessment (PPA) software component in the science data processing pipeline of the Kepler mission. The PPA performs two tasks: One is to analyze the health and performance of the Kepler photometer based on the long cadence science data down-linked via Ka band approximately every 30 days. The second is to determine the attitude of the Kepler spacecraft with high precision at each long cadence. The PPA component is demonstrated to work effectively with the Kepler flight data.

Li, Jie↗

NASA NTRS2010

Kepler Science Operations Center Pipeline Framework

The Kepler mission is designed to continuously monitor up to 170,000 stars at a 30 minute cadence for 3.5 years searching for Earth-size planets. The data are processed at the Science Operations Center (SOC) at NASA Ames Research Center. Because of the large volume of data and the memory and CPU-intensive nature of the analysis, significant computing hardware is required. We have developed generic pipeline framework software that is used to distribute and synchronize the processing across a cluster of CPUs and to manage the resulting products. The framework is written in Java and is therefore platform-independent, and scales from a single, standalone workstation (for development and research on small data sets) to a full cluster of homogeneous or heterogeneous hardware with minimal configuration changes. A plug-in architecture provides customized control of the unit of work without the need to modify the framework itself. Distributed transaction services provide for atomic storage of pipeline products for a unit of work across a relational database and the custom Kepler DB. Generic parameter management and data accountability services are provided to record the parameter values, software versions, and other meta-data used for each pipeline execution. A graphical console allows for the configuration, execution, and monitoring of pipelines. An alert and metrics subsystem is used to monitor the health and performance of the pipeline. The framework was developed for the Kepler project based on Kepler requirements, but the framework itself is generic and could be used for a variety of applications where these features are needed.

Klaus, Todd C.↗

NASA NTRS2010

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 planets

Wu, Hayley↗

NASA NTRS2010

Presearch Data Conditioning in the Kepler Science Operations Center Pipeline

We describe the Presearch Data Conditioning (PDC) software component and its context in the Kepler Science Operations Center (SOC) pipeline. The primary tasks of this component are to correct systematic and other errors, remove excess flux due to aperture crowding, and condition the raw flux light curves for over 160,000 long cadence (~thirty minute) and 512 short cadence (~one minute) targets across the focal plane array. Long cadence corrected flux light curves are subjected to a transiting planet search in a subsequent pipeline module. We discuss the science algorithms for long and short cadence PDC: identification and correction of unexplained (i.e., unrelated to known anomalies) discontinuities; systematic error correction; and excess flux removal. We discuss the propagation of uncertainties from raw to corrected flux. Finally, we present examples of raw and corrected flux time series for flight data to illustrate PDC performance. Corrected flux light curves produced by PDC are exported to the Multi-mission Archive at Space Telescope [Science Institute] (MAST) and will be made available to the general public in accordance with the NASA/Kepler data release policy.

Twicken, Joseph D.↗

NASA NTRS2010

Photometric Analysis in the Kepler Science Operations Center Pipeline

We describe the Photometric Analysis (PA) software component and its context in the Kepler Science Operations Center (SOC) pipeline. The primary tasks of this module are to compute the photometric flux and photocenters (centroids) for over 160,000 long cadence (~thirty minute) and 512 short cadence (~one minute) stellar targets from the calibrated pixels in their respective apertures. We discuss the science algorithms for long and short cadence PA: cosmic ray cleaning; background estimation and removal; aperture photometry; and flux-weighted centroiding. We discuss the end-to-end propagation of uncertainties for the science algorithms. Finally, we present examples of photometric apertures, raw flux light curves, and centroid time series from Kepler flight data. PA light curves, centroid time series, and barycentric timestamp corrections are exported to the Multi-mission Archive at Space Telescope [Science Institute] (MAST) and are made available to the general public in accordance with the NASA/Kepler data release policy.

Twicken, Joseph D.↗