lightcurver

Welcome to lightcurver! This is a photometry library leveraging STARRED, best used with time series of wide-field images. You can read more about it in the documentation.

Essentially, lightcurver provides the infrastructure to

• prepare a Point Spread Function (PSF) model for each wide-field image,
• precisely calibrate the relative zero point between each image.

This enables STARRED to model the pixels of the region of interest (ROI), yielding of course high quality light curves of the point sources in the ROI, but also recovering the subpixel information to provide a high signal-to-noise ratio/resolution of the ROI itself. The example below shows a cutout of a wide-field image (one in a set of a hundred), the fitted high resolution model, and the Hubble Space Telescope image of the same region.

Features

• Plate solving: uses Astrometry.net to establish the footprint of each frame.
• Gaia reference stars: leverages Gaia information to select the right reference stars in the field of view of each frame.
• Preserves sub-pixel information: never interpolates, essential to preserve the sub-pixel information that can be recovered by STARRED in a multi-epoch forward modelling.
• Incremental: uses SQL queries to dynamically determine which process needs be executed on which frame.
• Semi-automatic: create a yaml configuration file once for the first few frames, then run the pipeline whenever a new frame is added, providing auto-updating light curves.

Getting Started

1. Installation: the short version, install via pip:

   pip install lightcurver
   

   The slightly longer version, in case you plan on using a GPU or the plate solving.

2. Usage: Adapt the yaml config file template, then run the pipeline with

   lc_run /path/to/config.yaml

   The pipeline is incremental, but in a scenario of testing, you can run specific steps only, for example:

   lc_run /path/to/config.yaml --start stamp_extraction --stop psf_modeling

   (The names of the pipeline steps/tasks are listed upon running lc_run -h.)

3. Tutorial: follow the tutorial of the documentation, which provides a dataset you can experiment with. You can also test your installation with a subset of the dataset provided in the tutorial:

   cd /clone/of/lightcurver
   pytest .

Contributing

If you're encountering problems, then I would like to hear about them and will try to fix them. Feel free to create an issue in this repository. If you're finding this package useful and want to contribute, please create a pull request after forking the repository. If you need general help, feel free to reach out!

License

lightcurver is licensed under the GPL v3.0 License. See the LICENSE file for more details.

The implemented processing steps

This is an overview of the steps taken by the pipeline.