4 PI SKY goes hunting for more transients

This week, members from the 4 PI SKY team visited the AMI telescope in Cambridge, UK, in search of even more transients.

Members of the 4 PI SKY team visiting the AMI teelscope

Members of the 4 PI SKY team visiting the AMI telescope. Shown (from left to right): Clare Rumsey, Richard Saunders, Anthony Rushton, Tim Staley, Kunal Mooley, Rob Fender and Richard Armstrong.

The Universities of Oxford and Cambridge already have a very succesful partnership of following up astronomical transients at 15 GHz using the AMI large array. Gamma-ray alerts from the Swift-BAT space telescope robotically send messages back to earth-based servers, which in-turn automatically command AMI to slew to transient location in the sky (effectively eliminating the need of human intervention). However, when the array isn’t chasing high-energy explosions it spends a significant amount of time surveying galaxy clusters looking for Sunyaev-Zel’dovich (SZ) effects.

AMI recently completed the Tenth Cambridge Survey (10C; AMI Consortium: Davies et al. 2011; AMI Consortium: Franzen et al. 2011) at 15.7 GHz creating the deepest high-frequency (10 GHz) radio survey, complete to 1mJy in 10 different fields covering a total of≈27 deg^2. These data could contain radio transients that haven’t previously been found at other wavelengths and it is our goal to search the entire archive for historic events.

AMI-SA correlator

A new correlator that will power high-spectral resolution observations with AMI

In the mean-time, the AMI telescope is undergoing a major upgrade to the correlator. The original correlator was a lag-based system, which suffered from large errors in correlator lag spacing  and was prone to man-made radio frequency interferences (RFI) particularly at low declinations due to geostationary satellites.

The new AMI Digital Correlator (AMIDC), pictured right, will have a highly channelized digital correlator system giving more flexibility within the radio band and a much more uniform response across it, which would provide the potential to avoid or mitigate to a large extent many of the problems with the current system. This will significantly improve the sensitivity of the array.

Ultimately, we would like to use the new system to detect radio transients in near real-time and produce rapid VOEvent alerts that can help coordinate follow-up observations.

“TraP”, a transient detection pipeline, gets its first public release

We’re happy to announce that the TraP, an image-plane transients-detection pipeline, has reached version 2.0 and been made publicly available for the first time, along with an accompanying paper (to be published in Astronomy and Computing). The TraP project was born out of the UvA LOFAR-transients group, with close collaboration from the 4 Pi Sky team over the past few years.

The TraP is what’s known as a source-cataloguing pipeline, which means that it processes images one-by-one, extracting source positions and flux intensities from each image, then attempts to match up these measurements into lightcurves spanning the entire dataset. If something new pops up, or if a source shows significant variability in its lightcurve, the TraP will flag this up for further investigation. This approach has been used before (e.g. for the CRTS project), but the TraP is novel for a few reasons. Key among these is fact that TraP can be run on a plain-old stack of images, without the need for a carefully preprocessed ‘deep’ image (an image of unusually high quality made by summing the best images from a dataset) to seed the catalogue beforehand. This makes it possible to run the TraP on a sequence of images as they are observed in near real-time. It can also collate data from across a range of frequency bands, which is vital in the current era of wide-band observatories and multi-observatory observing campaigns.

A Banana screenshot

A screenshot from Banana, the web-based graphical interface for browsing TraP results.

Additionally, the TraP project has embraced some ways of working that are still quite unusual in astronomy. For starters, the graphical interface used to give an overview of results is a Django-powered web-interface. This means that while all the software and results are stored and run on a heavy-duty central server, end-users can still browse through the results in a intuitive manner using the web front-end, Banana (don’t ask). The TraP has grown into a significant software-project by most standards (~20,000 lines of Python and SQL code, ~350 unit-tests), with a diverse group of contributors. To ensure things kept running smoothly we employed comprehensive unit-testing, continuous integration, issue-tracking and code-review, which should put the project in good stead for a more open development model.

The TraP has already been put to good use on data from the LOFAR RSM, ALARRM, and JVLA-CHILES surveys (see our projects page). Hopefully this open release will allow profitable use with many more datasets.

Getting started with VOEvents

Have you ever wondered how astronomers share information between telescopes about rapidly evolving sources? Do you want to track gamma-ray bursts and stellar flares as they happen, maybe even point your telescope at them? Then you might be interested in VOEvents, and our new Python toolbox for dealing with them…

[Update April 2015: This post gives a gentle introduction to VOEvents. If you just want the technical details, see the new page about our VOEvent broker and how to connect to it.]


The VOEvent protocol can be used for passing alerts around a distributed, decentralized network of nodes.

The VOEvent protocol can be used for passing alerts around a distributed, decentralized network of nodes.
Image reproduced from J. Swinbank, Comet: A VOEvent broker, Astronomy and Computing, http://dx.doi.org/10.1016/j.ascom.2014.09.001

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