Project area/S
- DIA – Data Intensive Astronomy
Project Details
GC 2997 image taken while testing the new SPIRIT 6 telescope
AstroTarget AI will be a Python-based tool designed to assist high school students in selecting optimal astronomical targets for ICRAR’s robotic telescopes. Leveraging large language models (LLMs), the system will provide personalised recommendations based on students’ interests, current celestial events, and the specific capabilities of ICRAR’s robotic telescopes.
Key features will include:
- A natural language interface for students to input their preferences and objectives
- Integration with astronomical databases and real-time sky condition data
- Custom Python modules for celestial calculations and matching with ICRAR robotic telescope parameters
- LLM-powered explanations of recommended targets, enhancing educational value
The project will aim to make astronomy more accessible and engaging for high school students who use ICRAR’s robotic telescopes, while introducing them to practical applications of AI and programming in scientific research. It will specifically focus on optimising the use of ICRAR’s robotic telescopes for educational purposes.
Student Attributes
Academic Background
A student doing a degree with a significant Astronomy and/or Computer Science component
Computing Skills
Python, Machine Learning
Training Requirement
Pawsey Training on GPUs
Project Timeline
| Week 1 | Inductions and project introduction |
| Week 2 | Initial presentation |
| Week 3 | Investigate user interactions |
| Week 4 | Develop LLM interface |
| Week 5 | Develop interface to Astronomical database |
| Week 6 | Develop celestial calculations |
| Week 7 | Integrate the components |
| Week 8 | Integrate the components |
| Week 9 | Final presentation |
| Week 10 | Final report |
