# Research on Radionuclide Identification Method Based on GASF and Deep Residual Network
## 1 Introduction  
Nuclide identification is the process of determining the type of a nuclide by analyzing the radiation signals emitted by radioactive materials, such as gamma rays. Nuclide identification technology has a wide range of applications across various fields. In nuclear safety and non-proliferation, it is used for nuclear security checks, border control, monitoring nuclear materials during transportation, and preventing illegal entry into unsafe areas. In environmental monitoring, it helps track radiation contamination following nuclear accidents and aids in the management of nuclear waste. In the medical field, it is applied in diagnostic imaging, radioactive drug monitoring, and precise localization in radiationtherapy. In the nuclear energy sector, it enables real-time monitoring of nuclear reactor operations and nuclear fuel inspections. In scientific research, it is employed in astrophysics for studying elemental composition, in archaeology for isotopic dating, and in ecology for assessing radioactive contamination. In industrial applications, it is used in nuclear radiography, radioactive source detection, and ensuring the safe use of smoke detectors, among other applications [1; 2; 3; 4]. The accurate identification of radioactive materials is critical for preventing the illegal use of nuclear substances and ensuring public safety. The traditional nuclide library comparison method relies primarily on characteristics such as the position and area of spectral peaks, comparing these with a standard nuclide library to identify the nuclide type [5; 6; 7]. However, this method primarily focuses on spectral peaks, overlooks the full spectrum's potential, and is inefficient and cumbersome to apply. As such, traditional methods face significant challenges in accurately identifying nuclides. In recent years, with the rise of artificial intelligence, many researchers have applied machine learning and deep learning techniques to nuclide recognition, aiming to enhance recognition efficiency. Machine learning methods include logistic regression [8], fuzzy decision trees [9], singular value decomposition (SVD), support vector machines (SVMs) [10], and Bayesian networks [11], among others. Deep learning techniques include Long Short-Term Memory (LSTM) networks [12] and Convolutional Neural Networks (CNNs) [13]. [PERSON] employed logistic regression and wavelet packet decomposition to extract feature vectors from gamma spectrum data for training, achieving an identification accuracy of 97% [8]. [PERSON] developed a nuclide recognition algorithm based on singular value decomposition (SVD) and support vector machines (SVMs), converting one-dimensional gamma spectra into two-dimensional images, extracting feature vectors, and building a classifier. The recognition rate for nuclides reached 98% [10]. [PERSON] used Long Short-Term Memory (LSTM) networks for nuclide gamma spectrum identification and compared them with BP neural networks and Convolutional Neural Networks (CNNs). The average recognition rates on the test set were 83.45% and 86.21%, respectively, while the LSTM model achieved an average recognition rate of 93.04% [12]. However, traditional machine learning methods often rely on manual feature extraction and selection, which limits their ability to capture deep features and complex patterns in the data. In deep learning, most researchers typically input one-dimensional spectral data directly into models or convert it into grayscale images [14]. One-dimensional sequence data are often not subjected to feature extraction, making it difficult to capture complex patterns effectively. Furthermore, such data lack spatial structure and cannot fully utilize local features. Although grayscale images convey information through pixel values, their static nature hinders the extraction of time-dependent features, preventing the detection of subtle changes or higher-order patterns in the data.