A Review on the Application of Machine Learning in Gamma Spectroscopy: Challenges and Opportunities

Abstract

Gamma spectroscopy is an important analytical technique across various fields. Gamma spectroscopy uses the energy spectra of emitted gamma rays to examine the type and quantity of isotopes that exist in samples. Like any other analytical technique, traditional gamma spectroscopy encounters challenges that in some cases make the analysis uncertain. Machine Learning (ML) algorithms have been proposed as an approach for enhancing the precision and robustness of gamma spectroscopy. The current study introduces the basics behind ML and illustrates how they are applied in gamma spectroscopy using case studies. Major findings discussed herein demonstrate the developmental capability that ML has in improving gamma spectroscopy. Radioisotope identification, optimizing detector performance, and simplifying environmental monitoring processes have been the main areas in which ML algorithms have been deployed for improvement. These include the ability to predict and provide real-time spectrometry, among others, even though these opportunities come with their shortfalls such as the necessity for huge training datasets. This review explains that integrating ML into gamma spectroscopy marks a major change from current analytic techniques, with possible further developments in radiation detection and environmental science. It serves as very useful material for those studying or practicing AI and gamma spectroscopy and want to know more about it or need guidance on what is happening so far regarding AI integrated into gamma spectroscopy analysis.

1. Introduction

Gamma spectroscopy is an analytical technique that uses the energy spectra of gamma rays emitted by different radioactive materials. Gamma rays serve as a unique fingerprint of radioactive materials. By detecting and analyzing the energy distribution of these gamma rays, researchers can identify the type of radionuclides present and quantify their concentrations. Gamma spectroscopy depends on detectors such as semiconductors (e.g., high-purity germanium detectors) or scintillators (e.g., sodium iodide detectors) to capture the energy of gamma rays and generate a spectrum.

Gamma spectroscopy is an important technique in various fields such as nuclear physics [1], environmental monitoring [2,3,4,5], nuclear medicine [6,7], and homeland security [8,9]. In environmental science, gamma spectroscopy helps assess radioactive contamination levels, either from naturally occurring radioactive materials (NORMs) or artificial radioisotopes due to nuclear activities [10].

While gamma spectroscopy is an adaptable and widely used technique, it faces challenges. One serious limitation is the potential for overlapping gamma-ray full-energy peaks in complex samples [11,12], making it challenging to discriminate full-energy peaks and accurately identify and quantify multiple radionuclides in complex spectra. Generally, the precise measurement of gamma spectra requires detectors with high energy resolution such as semiconductor detectors (e.g., HPGe). Semiconductor detectors are expensive and require specific working conditions such as ultra-low temperatures. On the other hand, scintillators although cheaper and possessing easier setup and working conditions, they have low energy resolution. Additionally, variations in environmental conditions, such as background radiation, can introduce uncertainties and affect the accuracy of measurements [13].

Machine Learning (ML) algorithms have been proposed as a potential way to address the limitations of traditional gamma spectroscopy [14]. These algorithms could discriminate overlapping full-energy peaks well, thereby improving accuracy in radionuclide identification [15]. Also, ML algorithms could learn from changing environmental conditions, making them more robust against uncertainties and thus increasing the reliability of gamma spectroscopy measurements. The inclusion of Artificial Intelligence (AI) and Machine Learning (ML) into gamma spectroscopy is revolutionary for these long-standing issues. As technology develops, AI and ML play an increasingly crucial role not only in improving measurement accuracy but also in adapting to constantly changing environmental conditions by adding robustness against uncertainties.

The current paper covers fundamental concepts of AI and ML and their applications in gamma spectroscopy. This paper aims to investigate how ML algorithms can be utilized in practice, taken into account real-world examples and case studies that suggest coupling ML algorithms with traditional gamma spectroscopy would bring higher precision, efficiency, and reliability over traditional gamma spectroscopy. The authors of this paper hope to inspire greater innovation into the ever-evolving crossroads of analytical methodologies in nuclear science and environmental monitoring studies.

This paper has a broad appeal to audiences that are interested in nuclear science, environmentalists, people with AI/ML backgrounds, and researchers working on policymaking around nuclear security. This paper hopes to achieve this by meeting these objectives and therefore appeals as an interesting source for people who are curious about the interplay of gamma spectroscopy with AI.

2. A General Overview of Artificial Intelligence and Machine Learning

AI, ML, and deep learning (DL) are keywords that are being used frequently in almost all scientific fields. The relationship between AI, ML, and DL represents a hierarchical and interconnected relationship [16]. Fundamentally, AI is understood as a general concept that includes the development of computer systems qualified for performing tasks that have traditionally been associated with human intelligence [16,17]. ML, a subset of AI, deals with algorithms that allow machines to improve their performance on a specific task through learning from data. DL [17], in turn, is a specialized form of ML that allows for more complex and accurate data representations [18]. In future sections, we will cover the basics of AI, ML, and DL and clarify their functions. Figure 1 shows an overview of the relationship between AI, ML, and DL.

2.1. Artificial Intelligence (AI)

Artificial intelligence (AI) is a technology that enables computers to mimic human problem-solving and decision-making skills. AI not only mimics human intelligence but often surpasses it by obtaining higher levels of perception, reasoning, interaction, and learning. Like humans, AI systems in intelligent machines can autonomously adapt their behavior without explicit reprogramming, reflecting the ability to automatically change [16,17].

In this context, intelligence is defined as the ability to learn from the environment and apply the acquired knowledge in new situations. Humans have this inherent ability that distinguishes them from “non-intelligent” or non-autonomous machines [17]. Therefore, a machine that exhibits intelligence can process information from its environment and make decisions based on past data, thereby demonstrating its intelligent nature.

Using AI as a scientific discipline enables finding solutions to complex problems that require intelligent input. Currently, the demonstrations of AI in societies are diverse, with machines adept at identifying patterns in datasets through algorithmic analysis. AI facilitates learning from experiments and significantly contributes to the Internet of Things (IoT) and supports network applications in various domains [19,20,21].

2.2. Machine Learning

Machine Learning (ML) has inherently adaptive features that help evolve superior performance. This necessitates the development of interfaces that allow users to examine the behavior of AI systems and enable the system to explain its operating procedures. The advancement of AI is energized by the abundance of data available through the internet and advances in sensory technology (e.g., gamma spectroscopy), especially concerning ML [17]. ML models can be generally divided into two categories: shallow learning (SL) and deep learning (DL) [22]. Figure 2 provides a schematic overview of ML and its categories.

2.3. Shallow Machine Learning

There are three main types of shallow learning: (1) supervised learning; (2) unsupervised learning; and (3) reinforcement learning (RL) [22].

2.3.1. Supervised Shallow Machine Learning

Supervised learning is a type of ML in which machines are trained using well-“labeled” data, and based on that data, machines predict the output. The labeled data means some input data are already tagged with the correct output. Supervised learning is a process of providing input data as well as the correct output data to the ML algorithm. The goal of a supervised learning algorithm is to find a mapping function to map the input variable (x) with the output variable (y). Figure 3 shows the process flow of a typical supervised ML model.

Supervised Machine Learning comprises two fundamental categories as shown in Figure 2: regression and classification [22,23]. The selection of the appropriate category depends on the nature of the relationship between the input and output variables. In the case of regression, these algorithms are useful when predicting continuous variables, exemplified by applications. Several well-established regression algorithms fall within the realm of supervised learning, including logistic, linear, and polynomial regression.

On the other hand, classification algorithms are useful when dealing with categorical output variables and dividing samples into two or more classes such as yes–no, male–female, or true–false [22]. Prominent examples of classification algorithms include Naïve Bayes, k-nearest neighbors (KNNs), decision trees, and Support Vector Machines (SVMs).

2.3.2. Unsupervised Shallow Machine Learning

Unsupervised Machine Learning, as the name reflects, is different from supervised techniques. Unlike the supervised techniques, the models automatically identify hidden patterns and insights in the given data [22]. Unsupervised learning algorithms do not have corresponding output data, making them unsuitable for direct use in regression or classification problems. The main goal of unsupervised learning is to reveal the hidden structure of a dataset, group the data based on similarities, and combine the dataset in a compressed form. Figure 4 shows the processing flow of a typical unsupervised learning model.

Unsupervised learning is usually divided into two main methods: clustering and association as shown in Figure 2 [18]. Clustering is the process of grouping objects based on their similarities. Clustering techniques try to place the most similar objects into one cluster while identifying differences from objects in other clusters. By identifying similarities between data objects, clustering helps group them and illuminates hidden patterns and connections in the data. On the other hand, the association technique is mainly concerned with recognizing relationships between variables in large databases. It recognizes sets of items that are frequently associated with a dataset, thereby discovering patterns of association.

2.3.3. Reinforcement Learning

Reinforcement learning is distinguished as a feedback-based ML algorithm in which an agent, without labeled data, automatically learns how to navigate and act in an environment by receiving positive feedback for favorable actions and negative feedback or penalties for unfavorable practices. This unique approach to learning is particularly suited to sequential decision-making challenges with long-term goals. The agent automatically interacts with the environment, and the purpose of this interaction is to improve its performance by collecting maximum positive feedback. Reinforcement learning operates on the principle of trial and error, with the agent improving its behavior based on experience. Crucially, this is a fundamental component of artificial intelligence, allowing agents to learn and act without prior planning, thus demonstrating the flexibility and autonomy inherent in this learning paradigm [24].

2.4. Deep Learning

Deep learning (DL) provides a computational architecture to learn from data by combining multiple processing layers, such as input, hidden, and output layers. This architecture shares its basic principles with artificial neural networks (ANNs), which use connected layers of nodes to process data. However, ANNs are usually considered shallow learning algorithms because they may include only one or two hidden layers. When the number of hidden layers in an ANN increases, its ability to capture more complex features of a dataset increases. The main advantage of DL over shallow Machine Learning methods is its superior performance in various scenes, especially when learning from large datasets. Examples of DL include convergent neural networks (CNNs), and recurrent neural networks (RNNs).

2.5. How to Apply ML to Gamma Spectroscopy

Due to a sharp rise in the price of semiconductor detectors, researchers have been attracted to modifying scintillator detectors for elemental analysis. In a pioneering study by Doostmohammadi et al. [25], ANN was employed to identify the concentration of elements in a compound material. ANN can find the patterns in complicated input data to produce a gamma-ray spectrum with a better resolution.

To be more specific, an ML algorithm was applied to extract quantitative data from a gamma-ray spectrum which was not possible before due to the low resolution of the detectors. Monte Carlo simulations using the MCNP4C code generated gamma-ray spectra from interactions of fast and thermal neutrons with samples, encompassing 138 spectra of individual elements and their combinations. A spherical shell geometry with specific dimensions was simulated to model neutron and photon interactions, crucial for subsequent spectral analysis with NaI or BGO detectors. Concurrently, a multilayer ANN was implemented for gamma spectrum analysis, featuring an input layer with 1024 neurons representing spectral channels, a hidden layer optimized to 70 neurons for minimal error, and an output layer accommodating elements up to atomic number 82. Training employed the backpropagation algorithm to adjust network parameters iteratively, aiming to minimize discrepancies between the observed and expected outputs. The training of ANN focused on enhancing detection capabilities for elements like carbon and hydrogen, crucial for explosive material identification, and achieved precision exceeding 96% accuracy across 138 training spectra, using MATLAB’s SCG algorithm for efficient convergence [25].

A similar approach has been used by Gomez et al. to provide explanations to move from ordinary ANN to deep learning methods in isotope identification [26]. As mentioned above, NaI is considered a low-resolution detector that is eligible to employ ML for improving spectroscopic results. A similar approach is used in the study by Bandstra et al. using ML algorithms to distinguish I-131 from Ba-133 using the pertinent gamma-ray spectrum [27].

Figure 5 illustrates a typical flowchart for applying ML algorithms to enhance gamma spectroscopy results. Initially, gamma spectra are collected as raw data, either using a detector in a laboratory or through simulations. These spectra are aggregated to form a comprehensive database. The database is then refined by removing data with high uncertainties (noisy data). Typically, the refined database is split into two sets: one for training the model and the other for testing it. Next, an appropriate ML algorithm is selected based on the purpose and specifics of the problem. The chosen ML algorithm is then trained and tested, and the performance and accuracy of the model are assessed.

3. Commonly Used Machine Learning Methods in Gamma Spectroscopy

In this section, we give a comprehensive overview of the existing literature regarding the application of ML in gamma spectroscopy. This review is organized by ML algorithms. First, we provide a brief explanation of each algorithm, followed by a review of studies employing the corresponding ML algorithm. A list of the reviewed literature is provided in

Table 1. List of the reviewed literature in this study.

Reference	Aim of Study	ML Algorithm	Main Findings
[28]	Automate the identification and alarming of anomalous gamma-ray spectral readings with minimal human intervention.	Naïve Bayes	Successfully ranked medical isotope spectra above background levels, including weak signals like Cobalt spectra. Achieved accuracy ratio ranging between 0.91 and 0.99. Enhanced security applications and set a model for future advancements in public safety, improving threat detection while reducing false alarms.
[29]	Apply supervised ML for shape-sensitive detector pulse discrimination in positron annihilation lifetime spectroscopy.	Naïve Bayes	Applied Naïve Bayes classification model with high accuracy. Achieved accuracy of 0.98. Demonstrated that less than 20 labeled training pulses are sufficient to achieve comparable results. The model proves easy to implement.
[14]	Evaluate various ML algorithms for their effectiveness in identifying radioisotopes from gamma spectra, focusing on handling data complexities, accuracy, and efficiency.	Naïve Bayes, Support Vector Machines (SVMs), KNN, Decision tree (DT), MultiLayer Perception (ML)	The Naive Bayes classifier displayed vulnerability to errors with fluctuations and distortions, indicating limitations in handling complex or noisy data compared to other algorithms. SVM demonstrated better identification accuracy and minimal training and prediction times in datasets with smaller dimensions, encompassing five radioisotope types and 6000 spectra, considering factors such as data size and statistical fluctuation. KNN showed high accuracy (>0.9) but had significantly longer prediction times. For small-scale tasks involving up to six potential nuclides, SVM and LR were sufficiently accurate and consumed less time.
[30]	Overcome the challenge of site-specific calibrations for estimating soil texture using gamma-ray spectra in soil mapping and precision agriculture.	SVM	The SVM approach effectively predicted topsoil texture with high coefficients of determination (R2 = 0.96 for sand, R2 = 0.93 for silt, and R2 = 0.78 for clay). Most predictions exhibited absolute errors of less than 5%. Demonstrated the potential to provide highly resolved texture information at reduced costs and efforts, addressing limitations of traditional linear regression approaches.
[31]	Classify uranium waste drums to discriminate between those containing natural uranium and those with reprocessed uranium.	SVM	The SVM method demonstrated extraordinary accuracy, with only 4 out of 955 γ-ray spectra datasets showing discrepancies. Emphasized the efficiency of SVM in rapidly classifying data, supporting the scaling factor method and serving as a supplementary means to validate original labels in uranium waste management.
[32]	Enhance the performance of a time-of-flight neutron spectrometer by improving neutron detection accuracy.	SVM	SVM algorithm implemented in FPGA enabled real-time neutron sifting in mixed radiation fields. Achieved superior discrimination accuracy of 99.1%, outperforming the pulse gradient analysis method. Experimental evaluations confirmed the accuracy and performance of the SVM discriminator on the FPGA, demonstrating its versatility and effectiveness in advancing neutron detection capabilities.
[33]	Achieve the angular localization of gamma sources using a compact module with arrays of solid-state SiPM detectors, an integrated front-end, and a 3” LaBr3: Ce crystal.	K-NN	Successfully employed K-NN algorithms for the angular localization of gamma sources within the described compact module.
[34]	Integrate hyperspectral imagery and airborne gamma-ray spectroscopy data for a mineral exploration of the Sarfartoq carbonatite complex, focusing on REE and Nb mineralization.	DT	Successfully applied a DT algorithm to classify anomalies based on count per second (cps), integrating thorium and uranium anomalies with hyperspectral data analysis. Efficiently pinpointed and prioritized exploration targets for REE and Nb mineralization. Demonstrated the effectiveness of combining airborne hyperspectral and gamma-ray spectroscopy surveys for mineral exploration.
[35]	Develop a path-planning system for radioisotope identification devices using 4π gamma imaging to quickly and accurately identify radiation sources	Random Forest (RF)	RF analysis enhanced prediction model accuracy by recursively partitioning training data through multiple decision trees. Verified path-planning system through integrated simulation and experimentation with a Cs-137 point source, demonstrating competence in identifying sources using minimal measurement positions. Achieved an impressive prediction model accuracy of 86% through parameter tuning in RF analysis.
[36]	Evaluate DL algorithms for the automated identification of radioisotopes using NaI gamma-ray spectra	Fully Connected Neural Networks (FCNN), Recurrent Neural Networks (RNNs), Hybrid Neural Networks (HCNNs), Convolutional Neural Networks (CNNs), Gradient-Boosted Decision Trees (GBDTs)	FCNN models showed the fastest training and testing times. RNN and HRNN models required nearly 120 times more training time than FCNN. HCNN and GBDT models were five times slower than FCNN during training. NN models maintained constant runtime during testing, faster than GBDT models. HCNN demonstrated a 2-fold speed increase compared to HRNN during testing. HCNN and HRNN achieved high F1 scores with low percentages of training data, showing 5–20% improvement over other models with 5% data. RNN models exhibited higher standard deviations, indicating less stability than predictive models. The proposed hybrid DL architecture combining FCNN and DL models showed an advantage over existing methods in spectral data interpretation, with implications for nuclear security and threat detection.
[26]	Analyze monthly depositional fluxes of Be-7, Pb-210, and K-40 and their relationships with atmospheric variables using ML methods	DT, ANN	Both DT and NN models effectively reproduced radionuclide fluxes. ANN models achieved slightly better performance with higher mean Pearson-R coefficients (around 0.85) compared to DT models (0.83 for Be-7, 0.79 for Pb-210, and 0.80 for K-40). The application of ML algorithms enhanced the understanding of environmental processes and improved predictive capabilities in related studies.
[37]	Analyze gamma-ray spectra measured with a Germanium (Ge) spectrometer using ANNs.	ANN	ANNs effectively analyzed gamma-ray spectra, demonstrating high accuracy in identifying lines and their intensities. ANNs significantly reduced the time and effort required for spectral analysis compared to traditional methods. The performance of ANNs was comparable to or better than methods like peak search and least-squares fitting. ANNs showed versatility in analyzing spectra of mixed radioisotopes and uranium ores, successfully identifying the depletion of U-235 isotopes in reactor zone samples, consistent with mass spectrometry analysis.
[38]	Develop an algorithm to accurately identify single and multiple radioisotopes from gamma spectra of a plastic scintillator using ANN.	ANN	The algorithm achieved high identification accuracy: 98.9% for a single radioisotope and 99.1% for multiple radioisotopes showcasing robust performance in identifying various radioisotopes accurately.
[39]	Identify and quantify isotopes in gamma-ray spectra	FCANNs and CNNs	The study found high accuracy in simulated spectra with known background radiation and effectiveness in identifying full-energy peaks and shielding effects. The algorithm was sensitive to changes in background radiation. Convolutional neural networks (CNNs) performed better when dealing with unknown background radiation.
[40]	Estimate the depth and activity of radium contamination.	PCA, ANNs, SVMs	The study found that ANNs combined with lanthanum bromide detectors were the most accurate, with neural networks consistently outperforming SVMs. The AUC values ranged from 0.831 to 0.840 for NNs and 0.793 to 0.824 for SVMs. ML significantly improved radium contamination estimates and detector selection.
[41]	Estimate uranium concentration in ore samples.	Deep neural network (DNNs)	The study achieved satisfactory results with mean errors below 15% on small datasets and performed well on complex datasets with various uranium concentrations. ML predicted uranium concentration with uncertainties like classical methods (10–20%), offering a promising alternative that does not require expert knowledge.
[42]	Enhance radium contamination monitoring.	ANNs, SVMs	ANNs outperformed SVMs in separating background and source populations, resulting in an improved detection rate. The study evaluated various detector–algorithm combinations using spiked background spectra, demonstrating that ML, particularly NNs, enhances radium contamination monitoring.
[43]	Develop radioisotope identifiers for plastic scintillation detectors.	SVM, ANN, CNN	The study compared SVM, ANN, and CNN for radioisotope identification, emphasizing the influence of data normalization on prediction accuracy. Hyper-parameters were optimized to enhance SVM, ANN, and CNN performance. SVM achieved the highest accuracy, followed by ANN and CNN. SVM also had the shortest training time, while CNN required the longest. Despite its lower overall accuracy compared to SVM and ANN, CNN demonstrated consistent performance across different classes of radioisotopes.
[27]	Enhance transparency and trust in deep learning (DL) algorithms for gamma spectroscopy through explainable AI (XAI) methods.	Feed-forward and Convolutional Neural Network (CNN)	The study utilized saliency techniques to elucidate the decision-making process and identify critical input features. A network was developed to provide insights into the algorithm’s operations while achieving high classification accuracy. Visual representations of learned regions of interest were employed for performance evaluation, and heat vectors were introduced to highlight areas of interest and correlate them with physical characteristics such as energy peaks.
[44]	Estimate gamma ray direction using a feed-forward neural network.	Feed-Forward Neural Network	Demonstrated feasibility of using pre-amplified HPGe signals for source position determination. Achieved ~70% accuracy in predicting components (1000-pulse dataset). Highlighted network’s ability to learn patterns effectively.

, explaining the aim of the study, the ML algorithms used, and the main findings.

After reviewing the literature published, we noticed that supervised learning and deep learning algorithms have been used considerably more by researchers in the field of gamma spectroscopy.

To understand how a typical supervised ML model can be adapted in a gamma spectroscopy analysis, Figure 6 provides a casual flow diagram of this process. In this casual flow diagram, the predictive ML model is trained and tested using the labeled gamma spectra and a new spectrum can be introduced to the model and the model can predict the label for the new spectrum.

3.1. Naïve Bayes

The Naive Bayes algorithm is a simple yet powerful method for sorting data, based on Bayes’ theorem. It is generally used in supervised learning to classify data. This technique is applicable for designing ML models that are supposed to predict fast. The Naive Bayes algorithm predicts the likelihood of events. The term “naive” in Naive Bayes comes from its assumption of feature independence, wherein each feature is regarded as independent of others. This simplifies the sorting process by assuming that each feature operates separately to make decisions, despite this assumption not always holding true. However, despite its simple assumption, Naive Bayes demonstrates efficacy across numerous real-world scenarios, particularly with large and uncomplicated datasets [45].

Starting with the integration of ML algorithms in gamma spectroscopy, physicists at Health Canada (HC) tried to automate the identification and appropriate alarming of anomalous gamma-ray spectral readings with minimal human intervention. The results demonstrate the efficacy of the automated system in showing the successful ranking of medical isotope spectra above background levels, including weak signal strengths such as Cobalt spectra. Particularly remarkable is the utilization of the Naïve Bayes algorithm, emphasizing its crucial role in achieving these outcomes. The results based on the Naïve Bayes algorithm could achieve an accuracy ratio ranging between 0.91 and 0.99 (for more information about the metrics of the ML algorithms, please see

Table 2. Example of the confusion matrix for the evaluation of ML models. Actual class refers to the real labels. The predicted class refers to the labels assigned by the classification model based on its predictions.

	Predicted Class
Actual class	Positive (P)	Negative (N)
Positive (P)	True Positive (TP)	False Negative (FN)
Negative (N)	False Positive (FP)	True Negative (TN)

in Section 3.6). The result of this work not only enhances security applications of gamma spectroscopy but also sets a model for future advancements in public safety, showing the potential of ML to improve threat detection while reducing false alarms [28].

In another study, a Naive Bayes algorithm was employed for shape-sensitive detector pulse discrimination in positron annihilation lifetime spectroscopy. The study aimed to apply supervised ML to achieve this discrimination, and the main results indicated the successful application of the Naive Bayes classification model. The Naive Bayes classification model proves easy to implement. Notably, the study shows that a minimal number of less than 20 labeled training pulses is sufficient to achieve comparable results. An accuracy of 0.98 is achieved in this study [29]. The results of another research suggest that the Naive Bayes classifier displayed vulnerability to errors in the presence of fluctuations and distortions within the spectra. This implies that the Naive Bayes classifier may have exhibited limitations in handling complex or noisy data compared to other algorithms [14].

The Naïve Bayes algorithm presents promising potential for improving gamma spectroscopy applications. Its simplicity and effectiveness in classification tasks, rooted in Bayes theorem, make it a valuable tool for identifying radioisotopes, detecting anomalies, quantifying isotope concentrations, and separating signal from background noise in gamma-ray spectra. Naïve Bayes classifiers offer a robust approach to automating complex tasks in gamma spectroscopy with minimal human intervention. The successful integration of Naïve Bayes in gamma spectroscopy highlights its potential to improve nuclear security, environmental monitoring, and medical imaging applications by providing a rapid and accurate analysis of spectral data.

3.2. Support Vector Machine (SVM) Learning

The Support Vector Machine (SVM) algorithm is a ML method used for classifying data into different categories or predicting numerical values. It works by creating an optimal decision boundary between different data categories. Fundamentally, SVM aims to determine the best possible separation between data categories using a set of training data. This boundary is known as the Support Vector, typically defined as the data points closest to the decision boundary. The algorithm is named the Support Vector Machine because it optimizes the decision boundary using support vectors [46].

Gamma spectroscopy finds application in various industries, such as agriculture. Tobias Heggemann et al. explored its application in soil mapping and precision agriculture. The study aimed to overcome the challenge of site-specific calibrations for estimating soil texture using gamma-ray spectra. To improve precision, the researchers coupled gamma spectroscopy with Support Vector Machines (SVMs) for data pattern recognition. By utilizing the non-linear capabilities of SVM, the study seeks to develop a prediction model for topsoil texture, which is site-independent, addressing the limitations of traditional linear regression approaches. Through surveys across ten study sites in Germany and the collection of 417 reference samples, the researchers demonstrated the effectiveness of the SVM approach in achieving reliable texture estimations over different soil types with complex compositions. They used four gamma features (total counts, K-40, U-238, and Th-232) together as input variables to predict the sand, silt, and clay contents. The prediction models demonstrated high coefficients of determination, with R² = 0.96 for estimating sand content, R² = 0.93 for silt content, and R² = 0.78 for clay content. Most predictions exhibited absolute errors of less than 5%. The study shows the potential of gamma spectroscopy coupled with SVM to provide highly resolved texture information at reduced costs and efforts, offering valuable insights for applications in soil mapping and precision agriculture [30].

In a study focused on classifying uranium waste drums, researchers investigated the application of SVM for this task. The research aimed to discriminate between waste drums containing natural uranium and those with reprocessed uranium. Through training on 12 datasets and subsequent testing on 955 γ-ray spectra datasets acquired using NaI scintillation detectors, the SVM method showed extraordinary accuracy. Among the test datasets, only a mere 4 out of 955 exhibited discrepancies from the original labels, with one mislabeling and three misclassifications by SVM. These findings emphasize the efficiency of SVM in rapidly classifying data, making SVM a valuable algorithm for supporting the scaling factor method and serving as a supplementary means to validate original labels in uranium waste management processes [31].

Another application of the SVM algorithm is presented in a study focused on enhancing the performance of a time-of-flight neutron spectrometer. In this study, a novel neutron–gamma discriminator was introduced, utilizing the SVM algorithm to improve neutron detection accuracy. Notably, the SVM algorithm was implemented in a field programmable gate array (FPGA) to enable real-time neutron sifting in mixed radiation fields. A comparison between the pulse gradient analysis method and the SVM method revealed the superior discrimination accuracy of the SVM discriminator, achieving an impressive accuracy rate of 99.1%. Through experimental evaluations, the study confirmed the accuracy and performance of the SVM discriminator on the FPGA. This research highlights the versatility and effectiveness of SVM technology in advancing neutron detection capabilities, particularly in complex radiation fields [32].

In a research study focused on radioisotope identification using NaI gamma-ray detectors, the comparative performance of diverse ML algorithms was examined. The study considered factors such as data size and statistical fluctuation to describe the efficacy of algorithms. Findings from the analysis revealed that in datasets with smaller dimensions, encompassing six radioisotope types and 6000 spectra, SVM showed better identification accuracy alongside minimal training and prediction times [14].

In summary, the SVM algorithm serves as a powerful tool for classifying data and outlining optimal decision boundaries between different categories. Utilizing SVM in gamma spectroscopy applications demonstrates its efficacy in achieving accurate and efficient data classification.

3.3. K-Nearest Neighbor

The k-nearest neighbor (K-NN) algorithm is a straightforward supervised ML approach. It operates by assuming a resemblance between new and existing data points and assigns the new data point to the category most analogous to the available categories. Essentially, K-NN retains all available data, which facilitates the classification of new data points based on similarity. While it is versatile enough to handle both regression and classification tasks, it predominantly finds application in the classification. Characterized as a non-parametric algorithm, K-NN refrains from making assumptions about underlying data structures. During classification, K-NN dynamically categorizes new data points based on their similarity to existing ones [47].

In the K-NN algorithm, each data point in the feature space is assigned to a class based on the majority vote of its k-nearest neighbors. In simpler terms, the algorithm decides which class to assign to a point by considering the similarity of the nearest k points to the point of interest. This method is straightforward and easy to understand, making it a popular choice, especially for problems where the training data are not entirely separable. Moreover, K-NN serves as a powerful tool for noise detection and anomaly removal from datasets. In a comprehensive study comparing six ML algorithms for radioisotope identification using NaI gamma-ray spectra, the researchers delved into accuracy, efficiency, and systematic laws governing misidentification. The results showed that while KNN showcased high accuracy (greater than 0.9) across most datasets, its prediction time was one to three orders of magnitude longer than that of other algorithms. The paper concludes that for small-scale tasks, involving up to six potential nuclides, Support Vector Machines (SVMs) and logistic regression (LR) are sufficiently accurate while consuming less time [14].

In another study focused on gamma-ray detection, researchers have employed K-NN algorithms to achieve the angular localization of gamma sources. This approach is realized within a compact module featuring arrays of solid-state SiPM detectors, an integrated front-end, and a 3” LaBr3: Ce crystal. The experiment aimed to develop a microcontroller-based gamma spectrometer capable of reconstructing the direction of gamma photons and decentralizing position reconstruction computations. The system demonstrated sub-centimeter spatial resolution for single-direction position computations, using events within one FWHM from the Cs-137 photopeak. Despite computational challenges with the k-NN algorithm, decision tree (please see Section 3.4.) algorithms were effectively embedded, ensuring quick classification times. The system maintained an input count rate of 1.5 kcps, unaffected by the classification process due to the low computational complexity of the decision tree algorithm [33].

The K-NN algorithm offers a straightforward approach to supervised learning, primarily employed in classification tasks due to its reliance on data point similarity. Despite its versatility and utility in noise detection, K-NN has not been extensively utilized in gamma spectroscopy studies compared to other ML algorithms. Nonetheless, recent applications demonstrate its potential for tasks such as gamma source localization within compact detection modules, showing its adaptability across diverse domains within radiation detection.

3.4. Decision Trees (DTs)

A DT algorithm is an ML method that categorizes data into subsets based on a set of decision rules. It examines different features of the data and divides them into groups by making decisions at each step according to these features. In this algorithm, each node in the tree represents a feature of the data, and each branch represents a possible value of that feature. By passing over the tree from the root to the end nodes, the algorithm accurately assigns data samples to appropriate categories [48].

In a research study, DT algorithm were employed to integrate the results obtained from the analysis of hyperspectral imagery and airborne gamma-ray spectroscopy data for the mineral exploration of the Sarfartoq carbonatite complex. The article presents a systematic approach to identifying areas suitable for rare earth elements (REEs) and niobium (Nb) mineralization, utilizing gamma-ray thorium and uranium anomalies. It focuses on targets exhibiting high thorium values (>125 cps) and relatively low uranium values (<150 cps). A DT algorithm is employed to classify anomalies based on counts per second (cps). This methodology integrates thorium and uranium anomalies with hyperspectral data analysis to efficiently pinpoint and prioritize exploration targets for REE and Nb mineralization. The results indicate the successful application of DT, combining airborne hyperspectral and gamma-ray spectroscopy surveys for the mineral exploration of carbonatite complexes [34].

In a study conducted by Tomita et al., a path-planning system for radioisotope identification devices using $4\pi$ gamma imaging is presented. The objective of the system is to quickly and accurately identify radiation sources, a critical aspect in scenarios involving theft, the loss of sources, or potential acts of terrorism. The system leverages Random Forest (RF) analysis—a special form of DT—to enhance the accuracy of a prediction model. The RF analysis utilizes multiple DTs, recursively partitioning training data through hierarchical conditional branching. A notable outcome of this research is the verification of the path-planning system based on the prediction model through integrated simulation and experimentation involving a Cs-137 point source. The findings demonstrate the system’s competence in identifying Cs-137 point sources using a minimal number of measurement positions recommended by the path-planning system. Through parameter tuning in the RF analysis, an impressive prediction model accuracy of 86% was achieved [35].

3.5. Artificial Neural Networks (ANNs)

Artificial neural networks (ANNs) are computational models composed of a series of neurons that operate in a similar way to the human brain. These neurons are arranged in successive layers, and each neuron processes its inputs and produces an output. Information is transferred from one layer to another through weighted connections, and each layer performs its own specific operation, such as an activation function. During training, neural networks adjust their weights using training datasets and optimization algorithms to achieve the best performance in predicting outputs. After training, these networks can predict or classify new data. ANNs are currently used in various fields including image recognition, natural language processing, pattern recognition, prediction, and more due to their high computational power and ability to learn from data [49].

In a comparative study focusing on the automated identification of radioisotopes using NaI gamma-ray spectra, the authors delve into the latest developments in DL algorithms. The study evaluates various ML models, including fully connected neural networks (FCNNs), recurrent neural networks (RNNs), hybrid neural networks (HCNNs), convolutional neural networks (CNNs), and gradient-boosted decision trees (GBDTs). The findings indicated that FCNN models exhibited the fastest training and testing, while the RNN and HRNN required nearly 120 times more training time than the time required for FCNN model training. HCNN and GBDT models were five times slower than FCNN models during training. Interestingly, the runtime of the NN model remained constant during testing, proving faster than GBDT models. The HCNN also demonstrated a 2-fold increase in speed compared to the HRNN during test time. The results indicated that HCNN and HRNN models achieved high F1 scores with low percentages of training data, showing a 5–20% improvement over the other three models when trained with only 5% of the data. The standard deviation for the hybrid models remained below 1% under challenging training conditions (i.e., low percentages of training data). In contrast, RNN models exhibited high standard deviations compared to the other models, suggesting they are less stable than predictive models. Moreover, the authors proposed a hybrid DL architecture customized for spectral data interpretation, combining traditional FCNN and DL models. Under similar experimental conditions, the proposed method showcased advantages over existing methods, with implications extending to the realm of nuclear security and threat detection. This study underscores the potential of ML algorithms to automate the identification of radioactive materials, emphasizing their significance in enhancing security protocols [36].

In a study conducted in Malaga, Southern Spain, from 2005 to 2018, ML methods, including DT and NN algorithms, were employed to analyze the monthly depositional fluxes of Be-7, Pb-210, and K-40. The aim was to investigate their relationships with various atmospheric variables. The results showed that both DT and NN models effectively reproduced the radionuclide fluxes, with NN models achieving slightly better performance on average. Specifically, NN models yielded higher mean Pearson-R coefficients (around 0.85) compared to DT models (0.83, 0.79, and 0.80 for Be-7, Pb-210, and K-40, respectively). Overall, the application of ML algorithms enhanced the understanding of environmental processes and improved predictive capabilities in related studies [26].

Another noteworthy contribution to the field is presented in a paper introducing the application of ANNs for the analysis of gamma-ray spectra measured with a Germanium (Ge) spectrometer. Through comprehensive experiments and comparisons with other methods, the study uncovers key findings. Firstly, ANNs prove effective in analyzing gamma-ray spectra, demonstrating high accuracy in identifying lines and their intensities. Secondly, the use of ANNs significantly reduces the time and effort required for gamma-ray spectral analysis when compared to traditional methods. Thirdly, the performance of the ANN method stands comparable to or even surpasses other methods such as the peak search method and the least-squares fitting method. Lastly, the versatility of the neural network method extends to the analysis of gamma-ray spectra composed of mixed radioisotopes and the spectra of uranium ores, showcasing its applicability across diverse scenarios. The model successfully identified the depletion of U-235 isotopes in the samples from reactor zones, indicating past occurrences of fission reactions. The results were consistent with mass spectrometry analysis [37].

In yet another endeavor, a research study aimed to present an algorithm designed to accurately identify single and multiple radioisotopes from the gamma spectrum of a plastic scintillator, utilizing an ANN. The findings revealed the capability of the algorithm to achieve a high identification accuracy of 98.9% for a single radioisotope and 99.1% for multiple radioisotopes. This success is attributed to the use of Monte Carlo simulations for training the ANN, emphasizing the efficacy of this approach in gamma spectrum analysis by plastic scintillators [38].

In a comprehensive exploration of automated gamma spectroscopy methods, Kamuda et al. delved into the realm of pattern recognition algorithms, particularly ANNs and convolutional neural networks (CNNs). These algorithms emulated the cognitive processes of trained spectroscopists, leveraging prior knowledge of isotope emissions to match isotopes with gamma-ray spectra. The models exceled in identifying and quantifying isotopes by discriminating background and source full-energy peaks, calibrating against background full-energy peaks, and addressing shielding effects in low-energy and full-energy peaks. Key findings from the study shed light on the accuracy of both fully connected ANNs (FCANNs) and CNNs in quantifying isotopes within simulated spectra with known background radiation patterns. Sensitivity to changes in the background radiation field, the challenge of generalizing to real background radiation conditions, and nuanced behaviors related to standoff distances and full width at half maximum (FWHM) variations are highlighted. Notably, the study suggests that CNNs may be better suited to quantifying isotopes in spectra with unknown background radiation patterns [39].

In a separate endeavor by Varley et al., ML takes center stage in addressing radium contamination detection using gamma-ray spectrometry data. Conventional surveys often attribute higher activities to the largest total signal received, neglecting nuances in higher activities at depth. To overcome these limitations, the study employs Monte Carlo simulations, principal component analysis (PCA), and ML algorithms. The goal was to derive accurate depth and activity estimates for radium contamination. The study, conducted using two gamma-ray detectors (lanthanum bromide and sodium iodide) and identified a compelling solution: leveraging ANNs in combination with lanthanum bromide yields the most accurate depth and activity estimates. Among the two ML methods, neural networks (NNs) consistently outperformed Support Vector Machines (SVMs) across all detector configurations. The area under curve (AUC) values for NNs ranged from 0.831 to 0.840, while for SVMs, they ranged from 0.793 to 0.824. A higher AUC value indicates a more accurate model, with values closer to 1 indicating better performance. This underscores the role of ML in enhancing the precision of estimating radium contamination, guiding the selection of optimal detector and spectral processing routines [40].

In a study conducted by Allinei et al., the estimation of uranium concentration in ore samples took center stage, utilizing ML methods on HPGe gamma-ray spectra. The researchers employed a deep neural network algorithm, showcasing its efficacy despite a small dataset. The deep neural network model yielded satisfactory results with mean errors below 15%, even on a broader and more complex dataset encompassing uranium concentrations and experimental setups. Notably, the ML methods exhibited the potential to predict uranium concentration with uncertainties akin to classical gamma-ray spectroscopy (10% to 20%), offering a promising avenue without necessitating expert knowledge for spectrum interpretation [41].

Varley et al. investigated the realm of routine monitoring efforts for radium contamination, employing ML methods, specifically NNs and SVMs. By spiking background spectra with representative Monte Carlo source spectra, the study assessed the performance of different detector–algorithm combinations. Notably, NNs outshined SVMs, showcasing their efficiency in dividing background source populations and leading to a marked improvement in the detection rate. This study highlights the potential of ML to enhance routine monitoring efforts for radium contamination, with NNs emerging as a particularly effective tool in this context [42].

In another study by Jeon et al., ML methods, namely SVM, ANN, and CNN, were utilized to develop radioisotope identifiers for plastic scintillation detectors. The study offers a comprehensive comparison of the performances of these ML-based identifiers under different data normalization methods. Key findings include insights into the impact of data normalization on prediction accuracy, the determination of hyper-parameters for SVM, an ANN, and an CNN, and a detailed comparison of training times, revealing SVM as the fastest and the CNN as the slowest to train. The implemented radioisotope identifiers were evaluated using test sets obtained from experiments. Among the three ML-based identifiers, SVM exhibited the highest prediction accuracy, followed by the ANN and then the CNN. On the other hand, SVM required the longest training time, followed by the ANN and then the CNN. Specifically, SVM achieved the highest prediction accuracy for the combined test sets and had the shortest training time compared to the other two methods. In contrast, the CNN showed minimal variation in prediction accuracy across different classes, despite having the lowest overall prediction accuracy for the combined test sets among the three identifiers. This research provided valuable insights into the effectiveness of ML-based radioisotope identifiers and underscored the importance of data normalization in optimizing their performance [43].

In a study conducted by Gomez-Fernandez et al., the focus is on the application of DL, specifically feed-forward and CNN models, for isotope identification through gamma-ray spectroscopy. The primary objective is to validate the learning algorithm, thereby enhancing transparency and trust in its outcomes. In fact, researchers aim to understand the reason behind the results of the classification tasks using explainable AI (XAI). The saliency method has been used to investigate the parts of the input feature that have most impacts on the output results. The study shows noteworthy results, including the development of a network that elucidates the inner workings of the algorithm. Achieving remarkable accuracy in distinguishing spectra, the research employed visual representations of the network’s learned regions of interest to evaluate performance based on domain knowledge. Moreover, the study introduced the utilization of heat vectors as tools to highlight and correlate areas of interest with physical characteristics of the given information, such as energy peaks [26]. The complexities in applying explanation tools to spectral data are more thoroughly investigated in a study by Bandstra et al. [27].

A study by Varley et al. adopted an approach involving a feed-forward neural network to estimate the direction of gamma rays using a standard coaxial HPGe detector. The study demonstrates the feasibility of acquiring source position information from coaxial HPGe detectors, using direction-sensitive information embedded in the shape of pre-amplified HPGe signals. Notably, even without employing complex methodologies, the standard coaxial HPGe detector proves capable of estimating the direction of incoming gamma rays, providing initial guidance on the gamma-emitting radioactive source direction with reference to the detector. The results indicate a successful prediction of components with approximately 70% accuracy on the 1000-pulse set, emphasizing that the network is learning patterns rather than merely memorizing the data [44].

The widespread application of ANNs in various gamma spectroscopy studies underscores its significance in advancing research and automation. Through comparative studies and empirical investigations, it is evident that ANNs, alongside other ML algorithms, offer efficient solutions for complex tasks such as radioisotope identification and environmental analysis. The integration of ANNs with innovative techniques like Monte Carlo simulations and deep learning architectures further enhances their capabilities. Additionally, studies highlight the adaptability of ANNs across different detector types and experimental setups, showcasing their versatility and potential for addressing diverse research challenges. Moving forward, the continued exploration and refinement of ANN-based methodologies promise to catalyze advancements in gamma spectroscopy.

3.6. Metrics for Evaluation of ML Algorithms

In the context of gamma spectroscopy, where classification ML algorithms are extensively employed, it is important to understand the metrics used to evaluate these classification models. Here, we provide an overview of the key metrics for assessing the performance of classification models in gamma spectroscopy applications. The accuracy of an ML model can be assessed using the confusion matrix. A confusion matrix is a table that is often used to evaluate the performance of an ML classification model, particularly in supervised ML. It allows the visualization of the performance of an algorithm by comparing predicted classes with actual classes. The confusion matrix is typically a square matrix where rows represent the actual classes, and columns represent the predicted classes. The structure of the confusion matrix is represented in Table 2.

Several metrics could be obtained from the confusion matrix to further investigate the accuracy and performance of an ML model. The formula and description of these metrics are provided in

Table 3. Metrics, definitions, and formulas derived from confusion matrix. TP: True Positive, TN: True Negative, FP: False Positive, FN: False Negative.

Metrics	Formula	Description
Sensitivity	${\displaystyle \frac{TP}{TP+FN}}$	Sensitivity, also known as the true positive rate, measures the proportion of actual positive cases that are correctly identified by a diagnostic test or classification model.
Specificity	${\displaystyle \frac{TN}{FP+TN}}$	Specificity measures the proportion of actual negative cases that are correctly identified by a diagnostic test or classification model [50].
Precision	${\displaystyle \frac{TP}{TP+FP}}$	Precision is the proportion of true positive results among the total positive results (true positives plus false positives) produced by a classification model [51].
Negative Predictive Value (NPV)	${\displaystyle \frac{TN}{TN+FN}}$	Negative predictive value (NPV) is the proportion of true negative results among the total negative results (true negatives plus false negatives) produced by a classification model [51].
False Positive Rate	${\displaystyle \frac{FP}{FP+TN}}$	The false positive rate is the proportion of actual negative cases that are incorrectly classified as positive by a diagnostic test or classification model [51,52].
False Discovery Rate	${\displaystyle \frac{FP}{FP+TP}}$	The false discovery rate is the proportion of positive results that are false alarms among the total positive results produced by a classification model [53].
False Negative Rate	${\displaystyle \frac{FN}{TP+FN}}$	The false negative rate is the proportion of actual positive cases that are incorrectly classified as negative by a diagnostic test or classification model [52,53].
Accuracy	${\displaystyle \frac{TP+TN}{TP+TN+FP+FN}}$	Accuracy is the proportion of correct predictions (both true positives and true negatives) among all predictions made by a classification model [50].
F1 Score	${\displaystyle \frac{TP}{TP+\frac{1}{2}(FP+FN)}}$	The F1 score is the harmonic mean of precision and recall, providing a single score that balances both measures [50].
Matthews’ Correlation Coefficient	${\displaystyle \frac{\left(TP\times TN\right)-(FP\times FN)}{\sqrt{\left(TP+FP\right)\times (TP+FN)\times (TN+FP)\times (TN+FN)}}}$ The Matthews correlation coefficient (MCC) is a measure of the quality of binary classifications, considering true and false positives and negatives to produce a score between −1 and 1, where 1 indicates perfect prediction, 0 indicates a random prediction, and −1 indicates total disagreement between prediction and observation [54].

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4. Opportunities for Applying ML in Gamma Spectroscopy

• Predictive ability: ML algorithms, particularly decision trees and neural network algorithms, have demonstrated a strong predictive ability for reproducing depositional fluxes of radionuclides. This suggests an opportunity for using ML to enhance predictive modeling in environmental studies related to radionuclide behavior.
• Automated identification: ML methods, including SVM, KNN, and others, have shown high accuracy in radioisotope identification. This opens opportunities for developing automated systems capable of identifying and classifying gamma-ray spectra with minimal manual intervention.
• Optimization of detector performance: ML techniques, such as SVMs, have been employed to optimize the performance of HPGe detectors. ML can continue to play a role in improving the efficiency and resolution of gamma-ray detectors, impacting various fields including nuclear physics research.
• Directional gamma-ray spectrometry: ML algorithms, specifically k-NN and decision tree classifiers, have been used to achieve the angular localization of gamma sources. This presents an opportunity for the development of directional gamma-ray spectrometers with embedded ML, offering high spatial and energy resolution for applications such as nuclear spectroscopy and imaging.
• Background correction in low-activity measurements: ML has been applied for background estimation in low-count gamma-ray spectra. This opens avenues for developing adaptive and data-driven algorithms that automatically estimate background activity, enhancing accuracy in low-activity measurements.
• Real-time operation in spectrometry: Gamma-ray spectrometers with embedded ML algorithms offer real-time operation and distributed computational complexity. This presents opportunities for the development of portable, high-performance gamma-ray spectrometers suitable for various applications.
• Improving neutron–gamma discrimination: SVM has been employed for neutron-gamma discrimination with high true-positive rates. ML can contribute to enhancing the accuracy and speed of discrimination methods, improving the reliability of nuclear measurements in various contexts.

5. Open Challenges for Applying ML in Gamma Spectroscopy

• Computational efficiency: Varied running times were observed for different ML algorithms. Achieving a balance between accuracy and computational efficiency remains a challenge, especially when dealing with large datasets or real-time applications.
• Data normalization impact: In studies involving radioisotope identifiers, the impact of data normalization on prediction accuracy was emphasized. Understanding the influence of normalization methods on model performance is crucial for reliable results.
• Transparency and trust: In DL approaches, enhancing the transparency and trust of the algorithm decision-making process is a challenge. Understanding the inner workings of complex models, such as feed-forward and convolutional neural networks, is essential for practical applications, especially in isotope identification.
• Generalization of unknown conditions: ML models may face challenges in generalizing to unknown conditions. For instance, some studies found that models tended to overpredict or underpredict mixing coefficients when standoff distances deviated from those in the training dataset.
• Need for expertise: While ML methods show promise in predicting gamma-ray spectrometry data, some studies noted the need for expert knowledge in interpreting the results. Bridging the gap between ML specialists and domain experts is crucial for effective application.

6. Data Collection for ML in Gamma Spectroscopy and Other Spectroscopic Techniques

Gamma spectroscopy data collection differs significantly from other spectroscopic methods, primarily due to the nature of the data and the type of detectors used. In gamma spectroscopy, the data consist of discrete energy peaks corresponding to specific gamma-ray emissions, necessitating precise energy and efficiency calibration. This high energy resolution allows for the accurate identification and quantification of radionuclides. The detectors, such as high-purity germanium or scintillation detectors, require specific calibration and environmental considerations, such as background radiation subtraction. In contrast, other spectroscopic methods like UV-Vis, IR, and NMR produce continuous spectra and require more involved sample preparation and baseline corrections due to their sensitivity to ambient conditions [55].

These differences in data collection impact the application of machine ML algorithms. For gamma spectroscopy, ML algorithms focus on peak identification and deconvolution, utilizing discrete energy peaks as primary features. The simpler data structure facilitates straightforward classification and regression models. Conversely, continuous spectra from other spectroscopy methods demand extensive preprocessing, including baseline correction and normalization, and benefit from complex ML models like convolutional neural networks for pattern recognition.

7. Conclusions

Based on the review conducted in this paper, it is evident that ML algorithms play a crucial role in developing gamma spectroscopy across various domains. The key findings and insights from the reviewed papers shed light on the diverse applications and the potential challenges and opportunities associated with integrating ML into gamma-ray studies.

The papers reviewed encompass a wide array of applications, from environmental studies to nuclear physics research, highlighting the versatility and impact of ML algorithms in this field.

Challenges in applying ML to gamma spectroscopy include issues such as misidentification rates, sensitivity to changes in background radiation, and the need for extensive training datasets. Despite these challenges, the studies demonstrate the potential of ML to revolutionize processes such as radioisotope identification, the optimization of detector performance, and environmental monitoring.

The opportunities presented by ML in gamma spectroscopy are vast. ML algorithms exhibit strong predictive abilities, automate identification processes, optimize detector performance, and contribute to efficient energy management. Additionally, the development of directional gamma-ray spectrometers with embedded ML showcases the potential for real-time, portable, and high-performance spectrometry.

In conclusion, the integration of ML into gamma spectroscopy represents a transformative force, offering innovative solutions to complex problems. As technology continues to evolve, the synergy between gamma spectroscopy and ML is likely to lead to further breakthroughs, shaping the future landscape of radiation detection, environmental monitoring, and nuclear physics research. The journey to harnessing the full potential of ML in gamma spectroscopy is ongoing, promising continued advancements and discoveries in the realms of science and technology.