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BEGIN:VEVENT
DTSTAMP:20260509T235529Z
DTSTART;VALUE=DATE-TIME:20201207T130000
DTEND;VALUE=DATE-TIME:20201207T140000
SUMMARY:Sarah Bentley (Northumbria): Parameterised and probabilistic appr
 oaches to radial diffusion in Earth’s radiation belts
TZID:Europe/London
UID:20201207-8a1785d875b7928e0175d09d69d67c45@warwick.ac.uk
CREATED:20201202T153834Z
DESCRIPTION:Abstract: Twenty-first century life is highly dependent on sa
 tellite services\, which are at risk from the hazardous radiation belt e
 nvironment. Ultra-low frequency waves (ULF\, 1-10 mHz) are large-scale p
 lasma waves\, predominantly driven by the solar wind. These waves are re
 sponsible for the radial transport and energisation of electrons in Eart
 h's radiation belt\, and are therefore essential components of radiation
  belt modelling. Current models of ULF waves and the resulting radial di
 ffusion are deterministic\, producing a single output for each set of in
 put parameters. Meanwhile\, probabilistic modelling is used heavily in w
 eather and climate models to improve forecasting. These methods capture 
 some of the uncertainty inherent in a complex system\, accounting for th
 e effects of sub-scale processes and accurately representing the full ra
 nge of possible physical states more faithfully than by solely using the
  mean or median. We aim to apply these methods to better determine the i
 mpact of ULF waves on Earth’s radiation belts. By considering a paramete
 risation as an approximation of a manifold in our parameter space\, we f
 ind that random forests (a machine learning technique) inherently have t
 he properties for an empirical model which should mitigate typical space
  physics data issues such as sparseness\, interdependence and nonlineari
 ty. Our new\, freely available model is presented and we motivate the id
 ea of iterative hypothesis testing as a method of extracting physics fro
 m nonlinear empirical models. We examine the magnetic local time variati
 on of ULF wave power and find that remaining uncertainty in the model su
 ggests we need to capture the internal state of the magnetosphere for fu
 ture models.
LOCATION:
CATEGORIES:CFSA Seminar
LAST-MODIFIED:20201202T153834Z
ORGANIZER;CN=Anne-Marie Broomhall:
END:VEVENT
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