Resonant Spacetime Hypothesis 2.0

- Alternative: Gaussian smoothing with kernel \( \sigma = 5 \) Mpc/h.  

Step 2: Void Identification

Watershed Algorithm (ZOBOV/VIDE-like):  

```python  

from skimage.segmentation import watershed  

def find_voids(density_field, threshold=0.2):  

    # Threshold: / < 0.2 defines voids  

    mask = (density_field < threshold * np.mean(density_field))  

    labels = watershed(density_field, markers=None, mask=mask)  

    return labels  # Each label = 1 void  

```