## Unexpected amplification of fluctuations in the correlated field model by adding axes

I am working on a problem in which I might dynamically add axes and as such was relying on the normalization of the zero-mode in the correlated field model. I was surprised to notice that with each added axis the fluctuations of the correlated field get amplified by the inverse of `offset_std_mean`

. I am using NIFTy6 @ afc3e2df .

The following code snippets reproduces the described amplification

```
cf_axes = {
"offset_mean": 0.,
"offset_std_mean": 1e-3,
"offset_std_std": 1e-4,
"prefix": ''
}
temporal_axis_fluctuations = {
'fluctuations_mean': 1.,
'fluctuations_stddev': 0.1,
'loglogavgslope_mean': -1.0,
'loglogavgslope_stddev': 0.5,
'flexibility_mean': 2.5,
'flexibility_stddev': 1.0,
'asperity_mean': 0.5,
'asperity_stddev': 0.5,
'prefix': 'temporal_axis'
}
fish_axis_fluctuations = {
'fluctuations_mean': 1.,
'fluctuations_stddev': 0.1,
'loglogavgslope_mean': -1.5,
'loglogavgslope_stddev': 0.5,
'flexibility_mean': 2.5,
'flexibility_stddev': 1.0,
'asperity_mean': 0.5,
'asperity_stddev': 0.3,
'prefix': 'fish_axis'
}
cfmaker = ift.CorrelatedFieldMaker.make(**cf_axes)
cfmaker.add_fluctuations(ift.RGSpace(7638), **temporal_axis_fluctuations)
# cfmaker.add_fluctuations(ift.RGSpace(8), **fish_axis_fluctuations)
```

yields

```
cf = cfmaker.finalize()
np.mean([cf(ift.from_random(cf.domain)).s_std() for _ in range(100)]) # \approx 1
```

which is what I would expect. However, if I uncomment the last line in the second cell above, the result becomes

```
cf = cfmaker.finalize()
np.mean([cf(ift.from_random(cf.domain)).s_std() for _ in range(100)]) # \approx 1e+3
```

This does not make sense to me and I was expecting the same result as in the previous cell.

In short: Am I missing something or is there a bug in the correlated field model?