Deep Synoptic Array
Official@dsa110
Offers specialized radio astronomy processing, continuum imaging, and interferometric data calibration for large-scale astronomical observation pipelines.
Agent Skills by Deep Synoptic Array
Showing 31 vetted skills indexed across 1 GitHub repositories.
dsa110dsa110-python-development
Develop DSA-110 continuum imaging pipelines with Python 3.11+, Dagster, and FastAPI.
dsa110Organization Skill
Organize Measurement Set files into date-based directories and update the product database.
dsa110Spectral Index Skill
Compute spectral indices from multi-frequency radio astronomy images.
dsa110Visualization Skill
Convert FITS files to HDF5 IDIA and generate moment maps and PNG previews.
dsa110Pre-Imaging QA Skill
Validate phaseshifting and UV coverage in radio astronomy Measurement Sets.
dsa110Mosaic Skill
Orchestrate radio astronomy continuum mosaics from observation tiles with quality assessment.
dsa110GPU Acceleration Skill
Configure NVIDIA GPU acceleration for WSClean and CuPy in the DSA-110 imaging pipeline.
dsa110Pipeline Expert Advisor
Analyze DSA-110 radio astronomy pipeline code for calibration, imaging, and mosaicking improvements.
dsa110dagster-ui-migration
Migrate React components from Blueprint.js to Dagster's UI library.
dsa110External Package Documentation Skill
Search indexed documentation for radio astronomy Python libraries and CLI tools.
dsa110Calibration Skill
Calibrate DSA-110 radio interferometer data using CASA tasks.
dsa110ms-generation
Convert DSA-110 HDF5 subband visibility data into CASA Measurement Sets.
dsa110Imaging Skill
Generate continuum images from calibrated Measurement Sets using WSClean or CASA tclean.
dsa110Crossmatch Skill
Cross-match detected astronomical sources against radio source catalogs using Python scripts.
dsa110Gain-Only Calibration Skill
Calibrate radio astronomy mosaic tiles by reusing bandpass solutions with GPU-accelerated sky model prediction.
dsa110Catalog Setup Skill
Build and validate NVSS, FIRST, and RACS catalogs for radio astronomy declination strips.
dsa110Light Curve Skill
Compute Eta, V-index, and sigma-deviation variability metrics from time-series photometry data.
dsa110Variable Source Detection Skill
Compare daily photometry against baseline catalogs to detect variable and transient sources.
dsa110gtd-development-protocol
Enforce GTD methodology for agentic development workflows with defined Next Actions.
dsa110Validation Skill
Validate FITS astronomical images with astrometry, flux scale, and source count checks.
dsa110Photometry Skill
Perform adaptive binning photometry and calculate variability metrics for astronomical sources.
dsa110data-io-management
Manage ingestion, staging, metadata, storage layout, and HDF5 lifecycle for astronomical datasets.
dsa110calibration-imaging
Orchestrate radio astronomy calibration, flagging, imaging, and mosaicking workflows.
dsa110dev-workflows
Manage software pipeline workflows including tests, linting, and local dev servers.
Frequently Asked Questions About Deep Synoptic Array
FAQPage SchemaWhat specific astronomical tasks can be performed using these capabilities?▼
These capabilities enable radio astronomy data processing, including Measurement Set generation, continuum imaging, spectral index computation, and multi-frequency mosaic orchestration. Users can perform adaptive binning photometry, calculate variability metrics like Eta and V-index, and validate astronomical images against astrometry and flux scale requirements.
Who is the target persona for these radio astronomy processing skills?▼
The target personas are radio astronomers, data scientists, and research engineers working with the DSA-110 array. These individuals manage large-scale interferometric datasets, require high-performance imaging pipelines, and need to validate observational outputs against established astronomical catalogs like NVSS, FIRST, and RACS.
What are the primary dependencies for running the imaging pipeline?▼
The pipeline requires a Linux-based environment configured for Dagster, FastAPI, and CASA. Essential dependencies include WSClean for imaging, CuPy for GPU-accelerated sky model prediction, and HDF5 libraries for managing large-scale astronomical datasets. Users must also maintain valid Measurement Set structures and FITS-compliant metadata.