50 research outputs found
The Kepler DB, a Database Management System for Arrays, Sparse Arrays and Binary Data
The Kepler Science Operations Center stores pixel values on approximately six million pixels collected every 30-minutes, as well as data products that are generated as a result of running the Kepler science processing pipeline. The Kepler Database (Kepler DB) management system was created to act as the repository of this information. After one year of ight usage, Kepler DB is managing 3 TiB of data and is expected to grow to over 10 TiB over the course of the mission. Kepler DB is a non-relational, transactional database where data are represented as one dimensional arrays, sparse arrays or binary large objects. We will discuss Kepler DB's APIs, implementation, usage and deployment at the Kepler Science Operations Center
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
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
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
Semi-Weekly Monitoring of the Performance and Attitude of Kepler Using a Sparse Set of Targets
The Kepler spacecraft is in a heliocentric Earth-trailing orbit, continuously observing ~160,000 select stars over ~115 square degrees of sky using its photometer containing 42 highly sensitive CCDs. The science data from these stars, consisting of ~6 million pixels at 29.4-minute intervals, is downlinked only every ~30 days. Additional low-rate Xband communications contacts are conducted with the spacecraft twice a week to downlink a small subset of the science data. This paper describes how we assess and monitor the performance of the photometer and the pointing stability of the spacecraft using such a sparse data set
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
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 1 or 2 quarters.
From this set of targets we find a total of 5,392 detections which meet the
Kepler detection criteria: those criteria are periodicity of the 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 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