HAO Colloquium - Simon Wing, JHUAPL

Untangling the drivers of nonlinear systems with information theory

Many systems found in nature are nonlinear.  The drivers of the system are often nonlinearly correlated with one another, which makes it a challenge to understand the effects of an individual driver.  For example, solar wind velocity (Vsw) and density (nsw) are both found to correlate well with radiation belt fluxes and are thought to be drivers of the magnetospheric dynamics; however, the Vsw is anti-correlated with nsw, which can potentially confuse interpretation of these relationships as causal or coincidental.  Information theory can untangle the drivers of these systems, describe the underlying dynamics, and offer constraints to modelers and theorists, leading to better understanding of the systems.  Two examples are presented.  In the first example, the solar wind drivers of radiation belt electrons are investigated using mutual information (MI), conditional mutual information (CMI), and transfer entropy (TE).  As previously reported, Je is anticorrelated with nsw with a lag of 1 day.  However, this lag time and anticorrelation can be attributed mainly to the Je(t + 2 days) correlation with Vsw(t) and nsw(t + 1 day) anticorrelation with Vsw(t).  Analyses of solar wind driving of the magnetosphere need to consider the large lag times, up to 3 days, in the (Vsw, nsw) anticorrelation.  Using CMI to remove the effects of Vsw, the response of Je to nsw is 30% smaller and has a lag time < 24 hr, suggesting that the loss mechanism due to nsw or solar wind dynamic pressure has to start operating in < 24 hr.  Nonstationarity in the system dynamics is investigated using windowed TE.  The triangle distribution in the Je(t + 2 days) vs. Vsw(t) can be better understood with TE.  In the second example, the previously identified causal parameters of the solar cycle in the Babcock-Leighton type model such as the solar polar field, meridional flow, polar faculae (proxy for polar field), dipole axis strength, and flux emergence are investigated using TE.  The transfer of information from the polar field to the sunspot number (SSN) peaks at lag times of 3-4 years.  Both, the flux emergence and the meridional flow contribute to the polar field, but at different time scales.  The polar fields from at least the last 3 cycles contain information about SSN. 

Date and time: 
Wednesday, January 24, 2018 - 2:00pm to 3:00pm