[IGPP Everyone] CHANGE OF SEMINAR: TOMORROW 3:30 PM 6704 GEOLOGY Jacob Bortnik Machine Learning in Space Physics !!!

[MARCO VELLI]

Dear All, unfortunately the seminar by Xiaojia Zhang has had to be
postponed due to a medical emergency. While wishing Xiaojia the best for
her recovery, I would like to thank Jacob for offering to give a talk at
such short notice. I am therefore very happy to introduce tomorrow's
seminar:



SPACE PHYSICS SEMINAR

DEPARTMENT OF EARTH, PLANETARY, AND SPACE SCIENCES

DEPARTMENT OF ATMOSPHERIC AND OCEANIC SCIENCES

UNIVERSITY OF CALIFORNIA, LOS ANGELES


Jacob Bortnik, AOS, UCLA


Reconstruction Of Inner Magnetospheric Density, Waves, And Particle Fluxes
Based On A Neural Network Technique


The volume of space physics data continues to rise exponentially, and
promises to accelerate its growth in the near future to the point that
individual projects return on the order of a petabyte of data.  At the same
time, our analysis techniques have not kept pace with the rapid growth of
data, and often do not exploit the capabilities of the data to their
fullest potential.  In this talk, we present a novel method based on
machine learning technology, that aims to convert a sequence of point
measurements of some given quantity Q made over a long period of time (for
example observations made on a satellite), into a 3-dimensional dynamic
spatiotemporal model of that quantity.  As an example, we show a
three-dimensional dynamic electron density (DEN3D) model in the inner
magnetosphere, that can provide full coverage of the inner magnetosphere
and in fact is sufficiently accurate that it points the way to new physical
discoveries.  For instance, we report, an unexpected plasmaspheric density
increase at low L shell regions on the nightside during the main phase of a
moderate storm during 12-16 October 2004, as opposed to the expected
density decrease due to storm-time plasmaspheric erosion.  Since
plasmaspheric density values have been shown to be the largest source of
error in radiation belt models, we also show reconstructions of
whistler-mode chorus and plasmaspheric hiss waves, and show how these
models can be used as inputs to downstream models, that can subsequently
predict the dynamics of ‘data starved’ quantities, such as
ultra-relativistic electron fluxes.



Friday, OCTOBER 26th, 2018


Room 6704 Geology


3:30 - 5:00 PM
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