Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

TL;DR

The paper introduces an automated, real-time data processing system for the GPIES exoplanet survey that accelerates data reduction, improves workflow efficiency, and ensures uniform data quality for exoplanet detection and analysis.

Contribution

It presents the development of the Data Cruncher, an automated framework integrating multiple pipelines for rapid, uniform processing of GPIES data with cloud synchronization and user-friendly interfaces.

Findings

Reduced data processing time to less than an hour after data acquisition.

Enabled reprocessing of all data in a single day with pipeline improvements.

Facilitated real-time data visualization and communication with observers.

Abstract

The Gemini Planet Imager Exoplanet Survey (GPIES) is a multi-year direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow-up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our data reduction pipelines. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real-time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.