Fully automated transients follow-up systems will be vital in the coming era, when millions of transients per-night will be there for the finding in the datastreams of the LSST and SKA telescopes. One of 4 Pi Sky’s aims is developing the tools to enable this roboticization of early-time follow-up.
Towards this goal, we’re involved with software and algorithmic research efforts addressing the following sub-problems:
We are collaborating closely with the LOFAR-TKP team on development of the TraP, a transients-detection pipeline for image-data. TraP development is currently focused on processing images from radio surveys, but the underlying algorithms make it generally applicable to image-surveys at all wavelengths. A paper describing the first public release has been published in Astronomy and Computing.
Alerts distribution and processing
For some years now, VOEvent has been the generally agreed standard for disseminating astronomical transient alerts. We hook in to the VOEvent network using John Swinbank’s Comet. We have developed the voevent-parse package to make reading and manipulating VOEvent packets with Python simpler and more concise.
Automated data reduction
Experience has shown that it can be surprisingly challenging to operate radio-astronomy software in a fully automated manner, when it was originally designed for interactive use. We have developed Python packages to help bridge the gap between general purpose Python scripts and specialised data-reduction packages, for example in order to produce quick-look images of automatically triggered radio observations. See drive-ami and drive-casa for details.
Classification and smart follow-up scheduling
We are also working on the more abstract problems of transient classification and automated efficient follow-up, with one paper published (see this summary post) and more in prep – watch this space for updates.