FORCinel: An improved algorithm for calculating first‐order reversal curve distributions using locally weighted regression smoothing

We describe a modification to existing algorithms for the calculation of first‐order reversal curve (FORC) diagrams using locally weighted regression smoothing (often referred to as “LOESS” smoothing). The new algorithm offers several advantages over current methods: (1) it allows the FORC distribution to be calculated using a constant smoothing factor all the way to the H c = 0 axis; (2) noninteger values of the smoothing factor can be specified, enabling finer control over the degree of smoothing and the development of a graphical method for automated selection of the optimum smoothing factor; (3) it performs automated extrapolation across gaps or undefined regions of FORC space. This has two applications:…