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SUMMARY:Detection of Quasi-Periodic Eruptions in Extragalactic X-Ray Sourc
 es with Machine Learning
DTSTART;VALUE=DATE-TIME:20230630T154500Z
DTEND;VALUE=DATE-TIME:20230630T160000Z
DTSTAMP;VALUE=DATE-TIME:20260907T152831Z
UID:indico-contribution-839@cern.ch
DESCRIPTION:Speakers: Robbie Webbe (University of Bristol)\nQuasi-periodic
  eruptions (QPEs) are a novel phenomenon in high-energy astrophysics\, and
  to date have only been confirmed to be observed in a small number of AGN.
  Characterised by high amplitude variability over relatively short timesca
 les\, QPEs have the potential to provide insights into the strong gravity 
 regimes in the innermost regions of the accretion disks around AGN. To pro
 vide robust predictions of the physical mechanisms involved we need to fin
 d more QPE sources to broaden the understanding of the parameter space the
 y inhabit. We use known observations of QPEs and simulated lightcurves to 
 determine whether machine learning approaches can detect QPE sources\, and
  then apply these trained networks to the latest release of the XMM Serend
 ipitous Source Catalogue in the hunt for further candidates.\n\nhttps://in
 dico.unina.it/event/61/contributions/839/
LOCATION:Centro Congressi Federico II Aula Magna
URL:https://indico.unina.it/event/61/contributions/839/
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