A tool for extracting stellar photometry from widefield color or monochrome images of the night sky

[Full documentation](https://kpenev.github.io/AstroWISP/)

Currently the following photometry methods are supported, and in the future we plan to add Image Subtraction:

PSF and PRF fitting

Fit for the distribution of light from a star on the detector (usually constrained to vary smoothly across stars) and an individual amplitude for each star giving a measure of the flux.

Point Spread Function, or PSF, refers to the distribution of light hitting the detector as a function of the offset from the source center. In order to predict the value that a particular pixel should have given the PSF one needs to integrate over the pixel the product of the PSF and the sensitivity of the pixel at each position within the pixel.

Pixel Response Function, or PRF, incorporates the effect of the detector. The value of the PRF at a given offset from the source center gives the value a pixel centered at that location should have.

AstroWISP allows for both PSF and PRF fitting, imposing a requirement that either function depends smoothly on the properties of the star being fit. The dependence is parametrized as an arbitrary polynomial of functions of the relevant parameters. Which properties it is allowed to depend on is entirely up to the user. Typically at least the position of the source center on the detector is included, but other properties can be included as well (e.g. the color of the source, temperature of the telescope tube, etc.). Multiple images can be fit simultaneously imposing the smooth dependence both within and across images.

Aperture photometry

Sum-up the flux in a circular aperture centered around each source. AstroWISP handles pixels that span the aperture boundary by properly integrating the product of th PSF and the sub-pixel sensitivity.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.0.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for kpenev from gravatar.com kpenev

Credits

Author: Kaloyan Penev

Maintainer: Kaloyan Penev

License

MIT License (GNU GENERAL PUBLIC LICENSE)

Requires

Python >=3.11

Provides Extra

test visualize

Classifiers

Report project as malware

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