## 3 Analyses
### D Assay Interpolation  
Display of the assays on the 3D model was achieved by first selecting the assay locations as points in Blender v2.90.1. This was done by manually selecting the point locations on the helmet by comparing to photographs taken during data acquisition. These points were exported as a.csv file, which included their locations relative to the rest of the model (script included with supplementary data [22]). The pXRF data were related to the point locations in the.csv format. The point values were then interpolated onto the model using the PyVista toolkit v0.28.1 [25]. The field used for interpolation could be selected from those in the.csv file thus may have been any of the pXRF results or any other desired variable. PyVista uses a Gaussian kernel interpolation, which applies the values to the area within a specified radius of the point on the mesh. The code script and the data  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Assay**} & \multirow{2}{*}{**Location**} & **Mn** & **Fe** & **Ni** & **Cu** & **Zn** & **As** & **Sn** & **Sb** & **Pb** & **Compton** \\  & & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **cps** \\ \hline
1–5 & Front & 0.049 & 0.440 & 0.150 & 59.1 & 1.21 & 1.01 & 20.2 & 0.295 & 2.74 & 174.7 \\
6 & Front & 0.057 & 0.401 & 0.118 & 55.0 & 1.30 & 0.68 & 17.2 & 0.232 & 1.69 & 172.9 \\
7 & Front & 0.048 & 0.495 & 0.165 & 48.2 & 1.43 & 0.75 & 22.8 & 0.302 & 2.06 & 180.0 \\
8 & Front & 0.048 & 0.442 & 0.138 & 51.9 & 1.48 & 0.76 & 23.3 & 0.329 & 2.15 & 179.6 \\
9 & Front & 0.050 & 0.416 & 0.157 & 53.8 & 1.27 & 0.60 & 17.5 & 0.210 & 1.86 & 157.2 \\
1  
were included in the data repository for this paper [22]. The presentation in the Jupyter notebook included in the data also provided a view in 3D of the interpolated model.  
In Figure 5, we display views of the helmet with interpolation values of the pXRF analysis we determined as the most useful for examining the helmet, those being Fe, Sn, Pb, As, and the Compton values. The colours for each element were designated based on the maximum value, which is red in Figure 4. As there were no elements with a measurement of zero, the colour blue was treated as a null value and was also the minimum value on the scale. The interpolations were conducted at a radius of 5 cm so as to allow some blending of values while not over-representing the assay data on the object. As the search radius was 5 cm and the helmet was much thinner than that, in some cases under 1 mm, it did not distinguish well between different sides, which has implications for the interpretation of the different sheets the helmet is made from, as we discuss further below. However, it did provide an indication of the composite nature of the helmet as it presently exists, which is the result of both the initial production techniques and the post-depositional factors--most notably patination.  
The results of the interpolation demonstrate differences in the composition of the helmet. While, at first glance, it appears relatively homogenous, there are clearly differences between the crest and the bowl of the helmet and again towards the rim. Much of this difference almost certainly relates to variable patination processes, as the crest and the bowl of the helmet, on each side, are known to come from a single piece of hammered bronze. This is reinforced by the fact that the two halves of the helmet, which are made from separate pieces of bronze, seem to share broad similarities. Iron is present throughout the helmet but is higher on the rivet and towards the base of the crest. Lead is present in a higher proportion on the rivet but also along the main body of the helmet. The Compton results further demonstrate the relative homogeneity of the helmet with the exception of the prongs visible on the left view in Figure 5, which display a lower proportion of the elements displayed and a relatively lower density.