African VLBI Network Training Site


Invited Talk: Tuesday 24 April - Dr Griffin Foster

24 April 2018 09:00 - 10:00

Title: The Transient Radio Sky: Pulsars, FRBs & SETI

Author: Dr Griffin Foster
              Post-Doctoral Research Fellow
              University of Oxford, and 
              Visiting Scholar at the University of California,  

Some of the great mysteries of the Universe are hidden the momentary, transient signals that we find with our telescopes. Pulsars are the dense core of a collapsed star, rotating with incredibly accurate periods, like lighthouses in the sky. We even use pulsars as observatories to detect gravitation waves, measure the interstellar medium, and test general relativity. We are constantly searching for new ones in exotic environments, in complex orbits, and over a range periods and distances. In 2007, during such a search a bright pulse of radio light was detected a signal that appeared to occur far outside our own galaxy, indicating an extreme, and momentary event. This was the first of more than 30 such Fast Radio Bursts (FRBs) that has been detected so far. Today we still do not know what these unknown events are, but the mystery continues to unfold in exciting ways. An even larger mystery is the question of if we are alone in the Universe. Does only Earth host life? Are we the only species of transmitting technosignatures? The search for extraterrestrial intelligence (SETI) is an attempt to try to answer these questions using astronomical

More about the author:

Griffin Foster2

Griffin is a post-doctoral research fellow at the University of Oxford and a visiting scholar at the University of California at Berkeley as a member of Breakthrough Listen. His research focuses on the search for transients radio signals from pulsars, fast radio bursts, and ETI (extraterrestrial intelligence). He is involved with the commissioning of the MeerKAT, building digital backends for LOFAR stations, commensal searches for fast radio bursts using the Arecibo telescope and the Green Bank Telescope, and developing machine learning-based models to detect rare signals in noisy data.