We develop a new fitting method based on the maximization of the correlation between two curves or sets of discreet observations. We show that this correlation maximization fitting method is mathematically well defined under certain conditions. The key element is the sensitivity of the method - a measure of how localized the correlation maximum is. The most important advantage of the method is that it can be applied to disparate data sets that are expected to be correlated but not fitted to each other. The method is valuable in the analysis of space data sets from (1) physically remote sources that may have complicated and hidden causal linkages or (2) physically distinguished quantities that are reasonably connected. The derived possible relations can be examined by testing the correlation between their observational signals or other measurements. Finally, we examine data of density and temperature in the inner heliosheath, inferred from Interstellar Boundary Explorer observations, and show that the globally distributed flux of energetic neutral atoms represents a source plasma under isobaric thermodynamic processes. Key Points Development of the maximum correlation method of 2 sets of discreet observations A systematic way for applying the new method. We provide some examples. We explain why the method can be useful in space physics and geophysics.
All Science Journal Classification (ASJC) codes
- Space and Planetary Science
- Space data analysis