# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 4 Experimental Results and Analysis  
### Data Sources  
In nuclide identification research, obtaining experimental data presents numerous challenges. Firstly, the acquisition and management of radioactive sources involve high costs and strict safety regulations. Additionally, issues such as background noise, detector accuracy limitations, and sample imbalance affect data quality and model training. Furthermore, due to the wide variety of radioactive nuclides, it is not feasible to cover all possible nuclide combinations in a single experiment. In contrast, MCNP can generate gamma spectrum data for various nuclides under different experimental conditions. The reproducibility and controllability of MCNP allow for the collection of a large volume of training data without the constraints of laboratory settings. For example, [PERSON] et al. (2018) [PERSON] et al. (2018) used MCNP to simulate gamma spectrum data for different nuclides and successfully applied it in the training of deep learning models. In this way, MCNP provides reliable experimental data support for nuclide identification research, offering high feasibility and advantages. Therefore, this paper chooses to use MCNP to obtain gamma spectrum data. When using MCNP, background noise and detector efficiency, among other practical factors, are not considered. For high-resolution detectors, such as HPGe, the gamma spectra are clearer due to their higher energy resolution, making the spectral features more distinct. As a result, the task becomes relatively easier, which reduces the application value of neural networks. To introduce more challenges and enhance the effectiveness of neural networks in processing complex data, we choose to simulate the gamma spectra reactions of five single-source radionuclides using a NaI detector. These radionuclides include \({}^{137}\)Cs and \({}^{60}\)Co, which are widely used in nuclear power plants and radiation therapy; the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Calculation Method** & **Significance** \\ \hline Accuracy & A = (TP + TN)/(TP + FP + TN + FN) & The proportion of correct predictions among all predictions \\ Precision & P = TP/(TP + FP) & The proportion of predicted positive samples that are actually positive \\ Recall & R = TP/(TP + FN) & The proportion of actual positive samples that are correctly predicted \\ F1 Score & F1 = \(2\times\) R \(\times\) P/(R + P) & The harmonic mean of precision and recall \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model evaluation metrics. naturally-occurring radioactive isotopes \({}^{238}\)U and \({}^{232}\)Th; \({}^{226}\)Ra, which was once widely used in radiation therapy but has gradually been replaced by other radionuclides. Considering that the two main peaks of \({}^{60}\)Co are relatively close and that the decay chains of \({}^{238}\)U, \({}^{232}\)Th, and \({}^{226}\)Ra produce multiple gamma rays that may overlap with those of other radionuclides, we use these five single-source radionuclides to form two-way and three-way combinations to introduce more interference between the nuclides, thus simulating a more realistic radiation environment. This results in a dataset of mixed gamma spectra from two and three sources, totaling 25,870 spectra, with each spectrum consisting of 1024 channels. During the data simulation process, a NaI crystal with a cylindrical shape (diameter 6.52 cm, height 4 cm) was used as the detector. Different particle numbers (\(1\times 10^{4}\) to \(1\times 10^{7}\)) were set in the simulation, and various distances (1 cm to 18 cm) between the point source and the crystal were applied. To reflect the characteristic differences between the nuclides, different activity levels were assigned to the radionuclides: \({}^{60}\)Co, \({}^{137}\)Cs, and \({}^{226}\)Ra were set to an activity of \(1\times 10^{6}\) Bq, while \({}^{238}\)U and \({}^{232}\)Th had an activity of \(5\times 10^{6}\) Bq.