Pulsar Feature Lab
Pulsar Feature Lab
The pulsar feature lab application is a collection of python scripts useful for extracting machine learning features (otherwise known as scores or variables) from pulsar candidate files. The code was written in order to provide a tool-kit useful for designing and extracting new candidate features, whilst retaining the ability to extract existing features developed by the community at large. This enables newly conceived features to be evaluated with respect to existing features allowing an objective decision on their utility to be reached.
It is hoped this code base will be used by the radio astronomy community. By sharing features and the source code implementations used to extract them, existing and newly devised features can be evaluated together. A statistically optimal feature set can then be produced which maximises the performance of learning algorithms on observational data. This will assist all in isolating legitimate pulsar/transient/single-pulse detections in data collected around the world. Given the proliferation of observational data and the increase in data volumes to be expected from next generation radio telescopes such as the Square Kilometre Array (SKA), such collaboration is important if we are to avoid the 'big data' problems associated with other large science projects such as the Atlas Experiment at the Large Hadron Collider (LHC).
For more details of the toolkit please see the supplied user guide.
The pulsar feature lab scripts have the following system requirements:
Python 2.4 or later. SciPy NumPy matplotlib library
The application script PulsarFeatureLab.py can be executed via:
The script accepts a number of arguments. It requires four of these to execute, and accepts another three as optional (see the user guide).
This work was supported by grant EP/I028099/1 for the University of Manchester Centre for Doctoral Training in Computer Science, from the UK Engineering and Physical Sciences Research Council (EPSRC).
 R. J. Lyon et al., "Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach", in prep.
 R. P. Eatough et al., "Selection of radio pulsar candidates using artificial neural networks", Monthly Notices of the Royal Astronomical Society, vol. 407, no. 4, pp. 2443-2450, 2010.
 S. D. Bates et al., "The high time resolution universe pulsar survey vi. an artificial neural network and timing of 75 pulsars", Monthly Notices of the Royal Astronomical Society, vol. 427, no. 2, pp. 1052-1065, 2012.
 D. Thornton, "The High Time Resolution Radio Sky", PhD thesis, University of Manchester, Jodrell Bank Centre for Astrophysics School of Physics and Astronomy, 2013.
 K. J. Lee et al., "PEACE: pulsar evaluation algorithm for candidate extraction a software package for post-analysis processing of pulsar survey candidates", Monthly Notices of the Royal Astronomical Society, vol. 433, no. 1, pp. 688-694, 2013.
 V. Morello et al., "SPINN: a straightforward machine learning solution to the pulsar candidate selection problem", Monthly Notices of the Royal Astronomical Society, vol. 443, no. 2, pp. 1651-1662, 2014.