Available software
What to install to plan an observation or work with 7DT data, and what produced the data in the first place.
supy
supy is a collection of Python utilities for members and users of the 7DT survey. It covers the tasks that come up before and after an observation rather than the reduction itself: working out whether a target is observable, finding which tiles cover a position or a gravitational-wave localization region, and simulating the response of the filter set.
It is installed from source. Documentation, including worked examples for each module, is published at sdt-supy.readthedocs.io.
git clone https://github.com/7DimensionalTelescope/supy.git
cd supy
pip install .| Observer | Target visibility and altitude from El Sauce, including StarAlt-style plots |
|---|---|
| Tiles | Tile lookup by coordinate, matching against a localization region, and tile plotting |
| Simulator | Filter and detector response simulation for the 7DT bands |
| const | Instrument and site constants used by the other modules |
Running the pipeline yourself
Beyond its pipeline role, Py7DT is structured for offline reuse. Researchers inside and outside the 7DT team can run the same codebase to reprocess data with custom configurations, resuming from any stage of the reduction, and choose for themselves how far to trust the standard products. Images are passed through the pipeline as string paths with metadata in FITS headers and YAML files, rather than wrapped in a bespoke data model, which keeps products inspectable outside the pipeline and lowers the cost of learning to process 7DT data.
Py7DT uses a rolling-release version scheme in which the last digit is incremented whenever a scientific decision changes how data are processed. That version is recorded in every configuration file and in the process status database, so any product can be traced to the code that made it and reprocessed in bulk when the code changes. What each stage does is described under using the data.
The systems that produce the data
Three systems close observation, reduction and analysis into a nightly loop. They are not installed by external users, but knowing which one recorded a given quantity is often useful when interpreting it.
Array control, scheduling and target-of-opportunity response
Robotic Telescope Control System with Python
The autonomous operations framework that drives the array. Built on the TCSpy control layer, RTCSpy automates the whole night — startup, target selection, science observation, calibration acquisition, data transfer and shutdown — while coordinating all sixteen operational units over real-time network communication. A dedicated Target of Opportunity manager watches external alerts and can interrupt an ongoing observation, giving a response time of less than one minute between alert ingestion and the start of a follow-up exposure.
Preprocessing through difference imaging, at survey throughput
7DT data reduction pipeline
Py7DT reduces heterogeneous data from many telescope units, filters and observing modes while sustaining nightly survey throughput and minimizing latency for transient events. It succeeds gpPy-GPU, inheriting its scientific routines and choice of external engines but rebuilding the orchestration around them. Images are grouped by their properties into configurations, submitted to an SQLite-backed system queue, and dispatched in parallel according to priority and stage hierarchy — typically fifteen workers, three of them reserved for preprocessing.
System of record for observations, processing state and data quality
7DS data and operations database
A Django and PostgreSQL database that manages all 7DT data and integrates the operations of RTCSpy and Py7DT. Its survey schema is synchronized in real time and mirrors the operational state of the pipeline: process_status records the progress of every product along with its software version and any error codes, while image_qa holds quality metrics — seeing, ellipticity, 5σ depth, astrometric precision — for every image produced. A companion dependency table traces the provenance of any output back through coadds, processed singles, master frames and raw exposures.
Full technical descriptions are in Hyun et al., Py7DT: Data Reduction Pipeline of the 7-Dimensional Telescope (Proc. SPIE 14155-12), and Choi et al., Proc. SPIE 14151-12, which covers RTCSpy. See publications.