Robbie: A batch processing work-flow for the detection of radio transients and variables
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.gitignore
LICENCE.md
Makefile
README.md
auto_corr.py
calc_var.py
filter_transients.py
make_cube.py
make_mean.py
plot_lc.py
plot_transients.py
plot_variables.py

README.md

Robbie: A batch processing work-flow for the detection of radio transients and variables

Description

Robbie automates the process of cataloguing sources, finding variables, and identifying transients.

The workflow is described in Hancock et al. 2018 (submitted), and carries out the following steps:

  • Find sources in images
  • Compare these catalogues to a reference catalogue
  • Use the offsets to model image based distortions
  • Make warped/corrected images
  • Stack the warped images into a cube and form a mean image
  • Source find on the mean image to make a master catalogue
  • Priorized fit this catalogue into each of the individual images
  • Join the catalogues into a single table and calculate variability stats
  • Use the master catalogue to mask known sources from the individual images
  • Source find on the masked images to look for transients
  • Combine transients tables into a single catalogue, identifying the epoch of each detection

Configuration

You need to have the following software installed in order to use Robbie:

The included Makefile should be edited to set up some custom parameters. In particular you need to set:

  • STILTS = <however you would run stilts from the command line>
  • IMAGEFILE = a file that contains a list of all the images in epoch order (default=all_images.txt)
  • REFCAT = /path/to/your/external/reference/catalogue.fits
  • REGION = a MIMAS region file describing the region of interest.

Usage

Usage is: make [command | file]
 files:
  refcat.fits - a masked version of the external reference catalogue
  cube.fits - a stack of astrometry corrected images
  mean.fits - a mean image from the above stack
  flux_table_var.fits - light curves and variability stats for all persistent sources
  transients.fits - a catalogue of all candidate transient events
  transients.png - a visualisation of transients.fits

 commands:
  transients = transients.png
  variables = flux_table_var.fits
  sceince = variables + transients

I recommend that you `make science`

Credit

If you make use of Robbie as part of your work please cite Hancock et al. 2018 (submitted), and link to this repository.