Comet Hunter is an automated astronomical image ingestion and processing system designed to assist in the discovery of sungrazing comets from SOHO LASCO imagery.
NASA's Sungrazer Project enables the discovery and reporting of comets visible from the SOHO and STEREO satellites. To date, over five thousand comets have been discovered using the SOHO satellite. On board SOHO is the LASCO coronagraph, which consists of two telescopes — C2 and C3. Images from these telescopes are primarily used for reporting new comets.
For comet discovery, users rely on fragmented tools for downloading, processing, and reviewing imagery. There is no unified platform that automates the complete workflow from raw image availability to chronological playback of processed frames. Comet Hunter aims to bridge this gap.
- RAW images must be processed before becoming usable
- Sungrazer comets are often indistinguishable in single frames
- Chronological playback significantly improves detectability
- Most comets are reported within minutes of data availability.
- Time is critical.
The problem is not merely detection - it is rapid detection.
This requires a robust automation of the complete workflow: from RAW image ingestion to chronological playback of processed frames.
- Downlink slot synchronization
- Metadata ingestion from LASCO sources
- Parallel RAW image downloading
- Image processing pipelines for C2/C3
- Time-indexed frame retrieval
- REST API backend
- Scheduler-driven ingestion workflows
- Interactive frontend visualization
pip install comet-hunterStart the application
comet-hunter startCheck application status
comet-hunter statusStop the application
comet-hunter stopWhen started, the application will be available at:
http://localhost:8080
Application data, logs, and database files are stored in:
Windows:
C:\Users\<username>\.comet_hunter
Linux/macOS:
~/.comet_hunter
git clone https://github.com/AnandKri/comet-hunter.git
cd comet-hunterLinux/macOS
python -m venv .venv
source .venv/bin/activateWindows
python -m venv .venv
.venv\Scripts\activatepip install -r requirements.txtuvicorn backend.main:app --reloadpython frontend/app.py