Twicken, Joseph | Search NASA

NASA NTRS2019

Data Validation: Difference Imaging and Centroid Analysis

This Design Note describes the theory and application of difference imaging for automated validation of planet candidates in the Data Validation (DV) CSCI. This is one of the diagnostic tools employed in DV for planet candidate validation. The other DV diagnostics are described in separate KADNs and in the reference documents listed below. This document describes the difference image generation process, PRF-based centroiding of the out-of-transit and difference images, computation of quarterly centroid offsets and associated uncertainties, and determination of robust weighted mean centroid offsets/uncertainties over all available quarterly data sets. PRF-based centroiding is performed with calls to a library of functions that is shared with thePhotometric Analysis (PA) and Photometer Data Quality (PDQ) CSCIs. The centroiding library will be the subject of a separate Design Note.

Twicken, Joseph↗

NASA NTRS2010

Kepler Science Operations Center Architecture

We give an overview of the operational concepts and architecture of the Kepler Science Data Pipeline. Designed, developed, operated, and maintained by the Science Operations Center (SOC) at NASA Ames Research Center, the Kepler Science Data Pipeline is central element of the Kepler Ground Data System. The SOC 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 Data Pipeline, including the hardware infrastructure, scientific algorithms, and operational procedures. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center that hosts the computers required to perform data analysis. We discuss the high-performance, parallel computing software modules of the Kepler Science Data Pipeline that perform transit photometry, pixel-level calibration, systematic error-correction, attitude determination, stellar target management, and instrument characterization. We explain 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 Data Pipeline.

Middour, Christopher↗