Hi @lizellelubbe,

In my experience, soft padding is the most important property/parameter of masks used for refinement. It is most important for local refinements, when the mask typically excludes portions of the structure and not just the solvent. When working with small masks, Iâve observed similar phenomenons as pointed out here. Weâve updated our local refinement guide page with some specific notes/suggestions on mask padding for datasets.

I think that in part, the underlying explanation of your observations is due to signal processing issues. If the volume is thought of as a discrete 3D signal, then the application of a mask to the volume can be thought of as *windowing* the signal in order to exclude regions that we are not interested (windows are applied to a signal via multiplication, just like masks). In all refinements that follow the gold-standard FSC method of regularization and resolution assessment, we must assume that the Fourier coefficients with frequency larger than the initial lowpass resolution have shared signal corrupted by independent noise. The problem with masks is that they break that last assumption â using a common mask means that the noise in both half maps (after masking) is not independent. This compromises our ability to separate signal from noise, and hence, to reduce overfitting.

Based on the convolution theorem, the severity of this violation is directly related to the Fourier-space properties of the mask. In short, the more slowly the DFT of the mask falls off over frequency, the worse the violation will be. For example, a rectangular mask (i.e. one with *no soft padding*, regardless of dilation) has very slow falloff in Fourier space:

(from wikipedia). On the other extreme, the hann window (i.e. a âcosineâ window) has much faster falloff:

The closer the mask is to a hann window (i.e. the softer the falloff in real space), the more the noise in each half-map remains independent after masking, and thus we are more able to reliably detect resolution and limit overfitting. In practice, this means that any GSFSC-based method will require trading off precision in real space (how well the masked is focused on the particular domain of interest) and precision in Fourier space (required to prevent overfitting). Heavily prioritizing real-space precision leads to overfitting and artefacts â but heavily prioritizing precision in Fourier-space means the refinement is no longer focused on a specific domain of the structure. Right now, this trade off must be considered for each refinement, but we do have a helpful rule of thumb on the local refinement job page linked above that can be used as a starting point for a good softness level.

Best,

Michael