Million Points of Light (MPoL)#Star Discuss
MPoL is a Python framework for Regularized Maximum Likelihood (RML) imaging. It is built on top of PyTorch, which provides state of the art auto-differentiation capabilities and optimizers. We focus on supporting continuum and spectral line observations from interferometers like the Atacama Large Millimeter/Submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA). There is potential to extend the package to work on other Fourier reconstruction problems like sparse aperture masking and other forms of optical interferometry.
To get a sense of how MPoL works, please take a look at the Introduction to Regularized Maximum Likelihood Imaging and then the tutorials down below. If you have any questions, please join us on our Github discussions page.
If you’d like to help build the MPoL package, please check out the Developer Documentation to get started. For more information about the constellation of packages supporting RML imaging and modeling, check out the MPoL-dev organization website and github repository hosting the source code.
If you use MPoL in your research, please cite us! See MPoL-dev/MPoL for the citation.
- Introduction to Regularized Maximum Likelihood Imaging
- MPoL Installation
- Units and Conventions
- Developer Documentation
- Introduction to PyTorch: Tensors and Gradient Descent
- Gridding and diagnostic images
- Intro to RML with MPoL
- Likelihood functions and model visibilities
- Cross validation
- GPU Acceleration
- Initializing with the Dirty Image
- HD143006 Tutorial Part 1
- HD143006 Tutorial Part 2
- Making a Mock Dataset
- Parametric Inference with Pyro