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gmadet: GRANDMA detection pipeline

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Tools to help identification of transients in astronomical images.

Aim

  • Implementing a common detection pipeline for all the telescopes in the network.
  • Each relevant telescope characteristics into account through individual configuration files for each external software being used (Astromatic softwares, hotpants).
  • OS-independent, so that it can work on Windows, Mac OS and Linux machines.
  • Written in Python3, with some external C dependencies.

Features

  • Usage of Docker to allow its deployment on different OS. Container is an Ubuntu 18.04 image. Successfully tested on Windows, Linux and Mac OS.
  • Cosmics can be removed using the python library lacosmic.
  • Astrometric solution based on Gaia DR1 catalog computed using SCAMP.
  • Image alignment, stacking, resampling using SWarp.
  • Individual (part of) image PSF estimated with PSFEx.
  • Source extractions using SExtractor.
  • Image substraction with Pan-STARRS reference images using hotpants. PS1 images matching your image FoV are automatically downloaded.
  • Crossmatch of detected sources with Pan-STARRS DR1, GAIA DR2, GSC and USNO-B1 catalogs using the Xmatch algorithm, through astroquery.
  • Crossmatch of detected sources with solar system moving objects using SkyBot.
  • Detected sources not referenced in any catalog are then classified using a CNN network implemented with Keras, in order to keep only point-like sources.
  • Photometric calibration (in AB) using Pan-STARRS DR1, SDSS DR12 or GAIA DR2 based on the zeropoint computed over the whole image.
  • Automatic report of candidates to the database used for O3 using a HTTP POST method.

Installation

See documentation: https://gmadet.readthedocs.io.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

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