Overview
Caustics is a cutting-edge gravitational lensing pipeline that represents the future of astronomical data analysis. Built with GPU acceleration and automatic differentiation at its core, Caustics enables astronomers to analyze gravitational lensing phenomena with unprecedented speed and flexibility.
Key Features
- GPU-Accelerated: Leverage modern GPU hardware for dramatically faster computations
- Automatically-Differentiable: Built on PyTorch for seamless gradient computation and optimization
- Highly Modular: Flexible architecture allows researchers to compose custom lensing models
- Production-Ready: Designed for real-world astronomical research with scalability in mind
Technical Architecture
Caustics leverages the PyTorch ecosystem to provide both high performance and mathematical flexibility. The automatic differentiation capabilities enable advanced optimization techniques and parameter inference that would be prohibitively expensive with traditional methods.
Modern Software Stack
- Deep Learning Framework: PyTorch for GPU acceleration and autodiff
- Scientific Computing: Integration with standard Python scientific libraries
- Modularity: Component-based architecture for mixing and matching lensing models
Contributions as SSEC Principal Lead
Working with the eScience Institute's Scientific Software Engineering Center (SSEC), I contributed to:
- Modern software development practices and CI/CD pipelines
- Code review and architectural guidance
- Community engagement and open-source best practices
- Integration with the broader astronomical software ecosystem
Impact on Astronomy
Caustics represents a paradigm shift in gravitational lensing analysis:
- Speed: GPU acceleration enables analysis that was previously impractical
- Accuracy: Automatic differentiation provides exact gradients for optimization
- Accessibility: Modern Python stack lowers barriers to entry for researchers
- Reproducibility: Open-source development ensures transparent, reproducible science
Community & Collaboration
As an advocate for open-source scientific software at SSEC, I help facilitate:
- Community scientific software standards and best practices
- Collaboration across astronomy research groups
- Knowledge sharing through documentation and code examples
- Integration with the broader scientific Python ecosystem