Generally in a local refinement, because the amount of protein mass inside the mask may be very small, we don’t recommend trying to re-optimize the per-particle scale factors. By default, the local refinement job will use the already-optimized per-particle scale factors from the input particles, and those would have come from eg. a consensus refinement of the whole map, where there is enough signal to perform per-particle scale optimization.
So the histogram you are seeing is showing that the input particles already have a relatively broad distribution of per-particle scale factors (some having as much as 2x the contrast of others) and these factors are being used during reconstruction in local refinement.
You can definitely try to optimize the per-particle scales again during the local refinement (by turning on the parameter you mentioned), but you may see the results get worse. It’s hard to predict what will happen since it depends a lot on your data/mask/protein etc!