/PulsarFeatureLab

A framework useful for extracting machine learning features from pulsar candidate files (.PHCX and .PFD).

Primary LanguagePython

PulsarFeatureLab

Author: Rob Lyon, School of Computer Science & Jodrell Bank Centre for Astrophysics, University of Manchester, Kilburn Building, Oxford Road, Manchester M13 9PL. Chia Min Tan, Jodrell Bank Centre for Astrophysics, University of Manchester, Alan Turing Bulding, Oxford Road, Manchester M13 9PL.

Contact: rob@scienceguyrob.com or robert.lyon@postgrad.manchester.ac.uk Web: http://www.scienceguyrob.com or http://www.cs.manchester.ac.uk or alternatively http://www.jb.man.ac.uk

  1. Overview

    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.

    If you use the code in your work please cite us using (or see bibtex below):

    R. J. Lyon, B. W. Stappers, S. Cooper, J. M. Brooke, J. D. Knowles, Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach, Submitted to MNRAS.

  2. Requirements

    The pulsar feature lab scripts have the following system requirements:

    Python 2.4 or later. SciPy NumPy [matplotlib library] (http://matplotlib.org/)

  3. Use

    The application script PulsarFeatureLab.py can be executed via:

    python PulsarFeatureLab.py

    The script accepts a number of arguments. It requires four of these to execute, and accepts another three as optional.

    Required Arguments

    Flag Type Description
    −c integer Candidate file type.
    1. The Pulsar Hunter Candidate XML (PHCX) candidates output by the pipeline described by Morello et al.[6].
    2. The gnuzipped (‘.gz’) PHCX candidates produced by the pipeline described by Thornton [4].
    3. The prepfold (PFD) files output by the LOTAAS and similar surveys.
    −d string Integer Path to the directory containing candidates.
    -f string Path to the output file to create or append to.
    −t integer Type of features to generate.
    1. 12 features from Eatough et al. [2].
    2. 22 features from Bates et al. [3].
    3. 22 features from Thornton. [4].
    4. 6 features from Lee et al. [5].
    5. 6 features from Morello et al. [6].
    6. 8 features from Lyon et al. [1].
    7. Integrated (folded) profile data.
    8. DM-SNR Curve data.
    		 
    Optional Arguments
    Flag Type Description
    −−arff boolean Flag that when provided, writes feature output in the WEKA ARFF file format.
    −−meta boolean Flag that when provided, writes meta information, i.e. the candidate file name, to the output file.
    -v boolean Verbose debugging flag.

    1. Citing our work

      Please use the following citation if you make use of tool:

      @article{Lyon:2015:bs, author = {{Lyon}, R.~J. and {Stappers}, B.~W. and {Cooper}, S. and {Brooke}, J.~M. {Knowles}, J.~D.}, title = {{Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach}}, journal = {MNRAS}, volume = {000}, year = {2015}, pages = {000-000} }

    2. Acknowledgements

      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).

    3. References

      [1] R. J. Lyon et al., "Fifty Years of Pulsar Candidate Selection: From simple filters to a new principled real-time classification approach", Submitted to Monthly Notices of the Royal Astronomical Society.

      [2] 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.

      [3] 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.

      [4] D. Thornton, "The High Time Resolution Radio Sky", PhD thesis, University of Manchester, Jodrell Bank Centre for Astrophysics School of Physics and Astronomy, 2013.

      [5] 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.

      [6] 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.