Skip to main content
×
Home
    • Aa
    • Aa

Classification of ASKAP VAST Radio Light Curves

  • Umaa Rebbapragada (a1), Kitty Lo (a2), Kiri L. Wagstaff (a1), Colorado Reed (a3), Tara Murphy (a2) and David R. Thompson (a1)...
Abstract
Abstract

The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light-curve characterization and classification algorithms that are best suited for archival VAST light-curve classification. We perform our experiments on light-curve simulations of eight source types and achieve best-case performance of approximately 90% accuracy. We note that classification performance is most influenced by light-curve characterization rather than classifier algorithm.

    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Classification of ASKAP VAST Radio Light Curves
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about sending content to Dropbox.

      Classification of ASKAP VAST Radio Light Curves
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about sending content to Google Drive.

      Classification of ASKAP VAST Radio Light Curves
      Available formats
      ×
Copyright
References
Hide All
C. Cortes & V. Vapnik 1995, Machine Learning 20, p. 273

M. Hall , , 2009, SIGKDD Explorations 11, 1

J. R. Quinlan 1986, Machine Learning, 1, 1

J. W. Richards , , 2011, ApJ 733, 1

J. D. Scargle 1982, ApJ 263, p. 835

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Proceedings of the International Astronomical Union
  • ISSN: 1743-9213
  • EISSN: 1743-9221
  • URL: /core/journals/proceedings-of-the-international-astronomical-union
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 16 *
Loading metrics...

Abstract views

Total abstract views: 33 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 19th October 2017. This data will be updated every 24 hours.