Py7DT: data reduction pipeline of the 7-Dimensional Telescope
TL;DR
Current operational tests show that Py7DT can process a typical nightly survey volume within the daily time budget while producing prompt ToO products on an approximately one-hour end-to-end timescale when the target is immediately observable.
Abstract
We present Py7DT, the operational data reduction pipeline for the 7-Dimensional Telescope (7DT), a medium band optical telescope array in Chile designed for rapid target-of-opportunity follow-up and the 7-Dimensional Sky Survey of the entire southern sky. Py7DT addresses the main reduction challenge of 7DT: reducing heterogeneous data from many telescope units, filters, and observing modes while sustaining nightly survey throughput and minimizing latency for transient events. The pipeline orchestrates the processing flow by wrapping established astronomical software behind Python interfaces, using a stage-dependent image-grouping scheme, centralized path handling, and priority-aware scheduling. The code is modular and portable, serving as a standard tool for end users to reprocess 7DT data with custom configurations, and supports the diverse scientific goals of 7DT– from transient object search to pixel-based analysis of spatially resolved galaxies– while remaining flexible and easy to maintain. The pipeline also features multi-level logging, automatically generated quality-assessment statistics and flags, and web-based real-time status monitoring to reduce the management load on the small developer team. Current operational tests show that Py7DT can process a typical nightly survey volume within the daily time budget while producing prompt ToO products on an approximately one-hour end-to-end timescale when the target is immediately observable.