scholarly journals Bayesian Inference of Quasi‐Linear Radial Diffusion Parameters using Van Allen Probes

2020 ◽  
Vol 125 (5) ◽  
Author(s):  
Rakesh Sarma ◽  
Mandar Chandorkar ◽  
Irina Zhelavskaya ◽  
Yuri Shprits ◽  
Alexander Drozdov ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Radial Diffusion ◽  
Diffusion Parameters ◽  
Van Allen Probes

scholarly journals Electric and magnetic radial diffusion coefficients using the Van Allen probes data

2016 ◽  
Vol 121 (10) ◽  
pp. 9586-9607 ◽  
Author(s):  
Ashar F. Ali ◽  
David M. Malaspina ◽  
Scot R. Elkington ◽  
Allison N. Jaynes ◽  
Anthony A. Chan ◽  
...  
Keyword(s):  
Diffusion Coefficients ◽  
Radial Diffusion ◽  
Van Allen Probes

scholarly journals Calculating ultra-low-frequency wave power of the compressional magnetic field vs. <i>L</i> and time: multi-spacecraft analysis using the Van Allen probes, THEMIS and GOES

2016 ◽  
Vol 34 (6) ◽  
pp. 565-571 ◽  
Author(s):  
Theodore E. Sarris ◽  
Xinlin Li
Keyword(s):  
Diffusion Processes ◽  
Low Frequency ◽  
Time History ◽  
Energetic Particles ◽  
Radial Diffusion ◽  
Wave Power ◽  
Frequency Wave ◽  
Wave Numbers ◽  
Van Allen Probes ◽  
History Of

Abstract. Ultra-low-frequency (ULF) pulsations are critical in radial diffusion processes of energetic particles, and the power spectral density (PSD) of these fluctuations is an integral part of the radial diffusion coefficients and of assimilative models of the radiation belts. Using simultaneous measurements from two Geostationary Operational Environmental Satellites (GOES) geosynchronous satellites, three satellites of the Time History of Events and Macroscale Interactions during Substorms (THEMIS) spacecraft constellation and the two Van Allen probes during a 10-day period of intense geomagnetic activity and ULF pulsations of October 2012, we calculate the PSDs of ULF pulsations at different L shells. By following the time history of measurements at different L it is shown that, during this time, ULF wave power is not enhanced uniformly throughout the magnetosphere but instead is mostly enhanced in the outer L shells, close to the magnetopause, and to a lesser extent in the inner magnetosphere, closer to the plasmapause. Furthermore, by using phase differences between two GOES geosynchronous satellite pairs, we estimate the daily-averaged distribution of power at different azimuthal wave numbers. These results can have significant implications in better defining the effect of radial diffusion in the phase space density of energetic particles for different wave numbers or L shell distributions of ULF power.

Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

2015 ◽  
Author(s):  
Qing Dou ◽  
Ashish Vaswani ◽  
Kevin Knight ◽  
Chris Dyer
Keyword(s):  
Bayesian Inference ◽  
Vector Space ◽  
Vector Space Models

How do academics assess the results of multiple experiments?

2018 ◽  
Author(s):  
Olmo Van den Akker ◽  
Linda Dominguez Alvarez ◽  
Marjan Bakker ◽  
Jelte M. Wicherts ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Bayesian Inference ◽  
Secondary Analysis ◽  
Individual Participant Data ◽  
Additional Experiment ◽  
Underlying Theory ◽  
Individual Participant ◽  
Vote Counting

We studied how academics assess the results of a set of four experiments that all test a given theory. We found that participants’ belief in the theory increases with the number of significant results, and that direct replications were considered to be more important than conceptual replications. We found no difference between authors and reviewers in their propensity to submit or recommend to publish sets of results, but we did find that authors are generally more likely to desire an additional experiment. In a preregistered secondary analysis of individual participant data, we examined the heuristics academics use to assess the results of four experiments. Only 6 out of 312 (1.9%) participants we analyzed used the normative method of Bayesian inference, whereas the majority of participants used vote counting approaches that tend to undervalue the evidence for the underlying theory if two or more results are statistically significant.

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