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  • NAM2019
    • Registration
    • Key Dates & Outline Schedule
    • Practical Information
    • Exhibitors
    • Grants & Bursaries
    • Contacts
  • Science
    • Science Programme
    • Parallel Sessions
    • Plenary Talks
    • Community Session
    • Special Lunches
    • Posters
    • Presenter Guidelines
  • Social
    • What's On
    • Welcome Reception
    • RAS Awards Dinner
  • Media
  • Outreach
    • Outreach and Education Day
    • Fringe Event
    • School Visit Day
  • Lancaster
    • Travel
    • Accommodation
    • Childcare
    • Campus Map
    • About Lancaster
    • Code of Conduct

Thursday

Schedule

id
date time
AM
09:45
Abstract
Probabilistic classification of starburst and AGN radio emissions in the eMERGE Survey
Thursday

Abstract details

id
Probabilistic classification of starburst and AGN radio emissions in the eMERGE Survey
Date Submitted
2019-03-15 14:05:32
Nick
Wrigley
Jodrell Bank Centre for Astrophysics
Studying galaxy evolution from reionization to cosmic noon with the latest-generation multiwavelength facilities
Talk
Wrigley et al. (JBCA)
Radio emissions trace star-formation rates within galaxies using the well established Radio-FIR correlation and obviates the problem of dust extinction that plague some other wavebands. Hence radio imaging of star forming galaxies can provide a tracer of global star-formation rates if emissions can be disentangled from the contributions of Active Galactic Nuclei (AGN). We present a probabilistic categorisation of these two broad emission classes of sources, ubiquitously observed in deep fields. Whereas traditional classification methods have relied almost exclusively upon human judgement of individual objects, leading to a simple binary designation (or perhaps none at all), this method generates a classification probability based on metrics derived from high angular resolution measurements of sources. Using recent data from e-MERLIN's Galaxy Evolution (eMERGE) survey which probes ~800 star-forming galaxies and AGN to z~5, classification probabilities are derived revealing disentangled differential source counts. The method can be extended to include deeper multi-wavelength data to further decrease uncertainties at even earlier epochs, revealing global star formation rates as a function of cosmic time.

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