The use of artificial intelligence in gamma spectrum analysis / Wail Alaeddine Bakache

Public ISBD Titre : The use of artificial intelligence in gamma spectrum analysis Type de document : document électronique Auteurs : Wail Alaeddine Bakache, Auteur ; Ouassila Boukhenfouf, Directeur de thèse Editeur : Setif:UFA Année de publication : 2024 Importance : 1 vol (64 f.) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Physique Mots-clés : Gamma Spectrum Analysis Artificial Intelligence (AI) Artificial Neural Networks (ANNs) Sodium Iodide (NaI(Tl)) Detectors High-Purity Germanium (HPGe) Detectors Index. décimale : 530 - Physique Résumé : This thesis investigates the novel application of artificial intelligence (AI) in the study of gamma spectra. The primary objective is to develop an AI model that can convert spectra obtained from sodium iodide (NaI(Tl)) detectors to those resembling high-purity germanium (HPGe) detectors. This research includes a thorough examination of gamma-ray interactions with matter, the properties of several gamma-ray detectors, and the concepts of artificial neural networks (ANNs). The suggested model uses artificial neural networks (ANNs) to improve the resolution of gamma-ray spectroscopy in sodium iodide (NaI(Tl)) detectors. Note de contenu : Sommaire Chapter I. Interaction of Gamma Rays with Matter ................................................................................ 2 I.1. Introduction ....................................................................................................... 2 I.2. Electromagnetic Radiation ................................................................................ 2 I.3. Sources of Gamma Radiation ........................................................................... 3 I.3.1. Emission of Gamma Rays by Excited Nuclei .............................................................. 3 I.3.2. Annihilation Radiation .................................................................................................. 4 I.3.3. Emission of Gamma Radiation Following Nuclear Reactions ..................................... 4 I.3.4. Bremsstrahlung ........................................................................................................... 4 I.4. The Three Main Types of Gamma-Ray Interactions with Matter .................... 5 I.4.1. Photoelectric Absorption ............................................................................................. 5 I.4.2. Compton scattering ..................................................................................................... 6 I.4.3. Pair Production ............................................................................................................ 7 I.5. Attenuation of Gamma-ray ................................................................................ 8 I.6. Detector Response Function ............................................................................. 9 I.6.1. The very large detector .............................................................................................. 10 I.6.2. The very small detector ............................................................................................. 10 I.6.3. The ‘real’ detector ...................................................................................................... 11 I.6.4. Summary ................................................................................................................... 11 I.7. Complications in the Response Function........................................................ 12 I.7.1. Secondary Electron Escape ...................................................................................... 12 I.7.2. Characteristic X-Ray Escape .................................................................................... 13 I.7.3. Secondary Radiations Created Near the Source ...................................................... 13 I.7.4. Effects of Surrounding Materials ............................................................................... 14 I.7.5. Summation Peaks ..................................................................................................... 15 I.8. Standard Reference Sources for Gamma-Ray Spectroscopy .......................... 15 Chapter II. General characteristics of Gamma-Ray Detectors .............................................................. 17 II.1. Introduction ..................................................................................................... 17 II.2. Operating principles and chain of Gamma-ray spectrometry ......................... 17 II.3. Characterization of detector performance ....................................................... 18 II.3.1. Detector efficiency ..................................................................................................... 18 II.3.2. Dead time .................................................................................................................. 19 II.3.3. Background ............................................................................................................... 20 II.3.4. Energy resolution ...................................................................................................... 20 II.4. Types of Gamma-Ray Detectors..................................................................... 21 II.4.1. Scintillation detectors ................................................................................................ 22 II.4.2. Semiconductor detectors ........................................................................................... 25 II.5. Comparison between NaI (Tl) and HPGe detectors ....................................... 31 II.5.1. Energy resolution and efficiency ............................................................................... 32 Chapter III. Artificial Neural Networks: Concepts, Architectures ........................................................ 35 III.1. Introduction ..................................................................................................... 35 III.2. Historical Context and Evolution ................................................................... 35 III.3. Biological Foundations and Analogies ........................................................... 36 III.3.1. Biological Neuron ...................................................................................................... 36 III.3.2. Analogy ...................................................................................................................... 36 III.3.3. Artificial Neuron ......................................................................................................... 37 III.4. Artificial neurons and biological neurons ..................................................... 39 III.5. Basics of artificial neural networks ................................................................ 39 III.6. Learning Methods ........................................................................................... 40 III.6.1. Supervised learning ................................................................................................... 41 III.6.2. Unsupervised learning ............................................................................................... 41 III.6.3. Reinforced learning ................................................................................................... 41 III.7. Neural network training .................................................................................. 41 III.8. Activation Functions ....................................................................................... 41 III.9. Types of Artificial Neural Networks .............................................................. 42 III.9.1. Feedforward Neural Networks (FNNs) ...................................................................... 42 III.9.2. Convolutional Neural Networks (CNNs) .................................................................... 42 III.9.3. Recurrent Neural Networks (RNNs) .......................................................................... 42 III.10. Characteristics of neural networks .................................................................. 42 III.11. Software .......................................................................................................... 43 Chapter IV. Neural Network Modelling for Spectrum Analysis ........................................................... 44 IV.1. Introduction ..................................................................................................... 44 IV.2. Data Acquisition and Preprocessing ............................................................... 44 IV.2.1. Gamma-ray Spectra Sources .................................................................................... 44 IV.2.2. Data Collection and Calibration ................................................................................. 45 IV.2.3. Preprocessing Techniques ........................................................................................ 46 IV.3. Artificial Neural Network Architecture for NaI(Tl) to HPGe-like Spectra Conversion 47 IV.3.1. Network Design (Input, Hidden Layers, Output)........................................................ 47 IV.3.2. Activation Functions and Training Parameters ......................................................... 48 IV.4. Data Preparation ............................................................................................. 51 IV.4.1. Reading Data ............................................................................................................. 51 IV.4.2. Normalizing Spect ..................................................................................................... 51 IV.4.3. Visualizing Spectra .................................................................................................... 52 IV.5. Model Preparation........................................................................................... 52 IV.5.1. Data Truncation ......................................................................................................... 52 IV.5.2. Feature and Target Matrices ..................................................................................... 53 IV.5.3. Splitting the Data ....................................................................................................... 53 IV.6. Neural Network Model ................................................................................... 53 IV.6.1. Model Design............................................................................................................. 53 IV.7. Model Compilation ......................................................................................... 54 IV.7.1. Callbacks ................................................................................................................... 54 IV.8. Model Training ............................................................................................... 54 IV.9. Model Evaluation ............................................................................................ 54 IV.10. Model Performance......................................................................................... 55 IV.10.1. Training and Validation Loss: ............................................................................... 55 IV.10.2. Test Loss and Accuracy ....................................................................................... 55 IV.10.3. Spectra Comparison ............................................................................................. 56 IV.11. Discussion ....................................................................................................... 58 IV.11.1. Model Efficacy ...................................................................................................... 58 IV.11.2. Potential Improvements ........................................................................................ 58 IV.12. Conclusion ...................................................................................................... 58 Conclusion............................................................................................................................................ 59 Bibliography ......................................................................................................................................... 61 Côte titre : MAPH/0654 The use of artificial intelligence in gamma spectrum analysis [document électronique] / Wail Alaeddine Bakache, Auteur ; Ouassila Boukhenfouf, Directeur de thèse . - [S.l.] : Setif:UFA, 2024 . - 1 vol (64 f.) ; 29 cm. Langues : Anglais (eng) Catégories : Thèses & Mémoires:Physique Mots-clés : Gamma Spectrum Analysis Artificial Intelligence (AI) Artificial Neural Networks (ANNs) Sodium Iodide (NaI(Tl)) Detectors High-Purity Germanium (HPGe) Detectors Index. décimale : 530 - Physique Résumé : This thesis investigates the novel application of artificial intelligence (AI) in the study of gamma spectra. The primary objective is to develop an AI model that can convert spectra obtained from sodium iodide (NaI(Tl)) detectors to those resembling high-purity germanium (HPGe) detectors. This research includes a thorough examination of gamma-ray interactions with matter, the properties of several gamma-ray detectors, and the concepts of artificial neural networks (ANNs). The suggested model uses artificial neural networks (ANNs) to improve the resolution of gamma-ray spectroscopy in sodium iodide (NaI(Tl)) detectors. Note de contenu : Sommaire Chapter I. Interaction of Gamma Rays with Matter ................................................................................ 2 I.1. Introduction ....................................................................................................... 2 I.2. Electromagnetic Radiation ................................................................................ 2 I.3. Sources of Gamma Radiation ........................................................................... 3 I.3.1. Emission of Gamma Rays by Excited Nuclei .............................................................. 3 I.3.2. Annihilation Radiation .................................................................................................. 4 I.3.3. Emission of Gamma Radiation Following Nuclear Reactions ..................................... 4 I.3.4. Bremsstrahlung ........................................................................................................... 4 I.4. The Three Main Types of Gamma-Ray Interactions with Matter .................... 5 I.4.1. Photoelectric Absorption ............................................................................................. 5 I.4.2. Compton scattering ..................................................................................................... 6 I.4.3. Pair Production ............................................................................................................ 7 I.5. Attenuation of Gamma-ray ................................................................................ 8 I.6. Detector Response Function ............................................................................. 9 I.6.1. The very large detector .............................................................................................. 10 I.6.2. The very small detector ............................................................................................. 10 I.6.3. The ‘real’ detector ...................................................................................................... 11 I.6.4. Summary ................................................................................................................... 11 I.7. Complications in the Response Function........................................................ 12 I.7.1. Secondary Electron Escape ...................................................................................... 12 I.7.2. Characteristic X-Ray Escape .................................................................................... 13 I.7.3. Secondary Radiations Created Near the Source ...................................................... 13 I.7.4. Effects of Surrounding Materials ............................................................................... 14 I.7.5. Summation Peaks ..................................................................................................... 15 I.8. Standard Reference Sources for Gamma-Ray Spectroscopy .......................... 15 Chapter II. General characteristics of Gamma-Ray Detectors .............................................................. 17 II.1. Introduction ..................................................................................................... 17 II.2. Operating principles and chain of Gamma-ray spectrometry ......................... 17 II.3. Characterization of detector performance ....................................................... 18 II.3.1. Detector efficiency ..................................................................................................... 18 II.3.2. Dead time .................................................................................................................. 19 II.3.3. Background ............................................................................................................... 20 II.3.4. Energy resolution ...................................................................................................... 20 II.4. Types of Gamma-Ray Detectors..................................................................... 21 II.4.1. Scintillation detectors ................................................................................................ 22 II.4.2. Semiconductor detectors ........................................................................................... 25 II.5. Comparison between NaI (Tl) and HPGe detectors ....................................... 31 II.5.1. Energy resolution and efficiency ............................................................................... 32 Chapter III. Artificial Neural Networks: Concepts, Architectures ........................................................ 35 III.1. Introduction ..................................................................................................... 35 III.2. Historical Context and Evolution ................................................................... 35 III.3. Biological Foundations and Analogies ........................................................... 36 III.3.1. Biological Neuron ...................................................................................................... 36 III.3.2. Analogy ...................................................................................................................... 36 III.3.3. Artificial Neuron ......................................................................................................... 37 III.4. Artificial neurons and biological neurons ..................................................... 39 III.5. Basics of artificial neural networks ................................................................ 39 III.6. Learning Methods ........................................................................................... 40 III.6.1. Supervised learning ................................................................................................... 41 III.6.2. Unsupervised learning ............................................................................................... 41 III.6.3. Reinforced learning ................................................................................................... 41 III.7. Neural network training .................................................................................. 41 III.8. Activation Functions ....................................................................................... 41 III.9. Types of Artificial Neural Networks .............................................................. 42 III.9.1. Feedforward Neural Networks (FNNs) ...................................................................... 42 III.9.2. Convolutional Neural Networks (CNNs) .................................................................... 42 III.9.3. Recurrent Neural Networks (RNNs) .......................................................................... 42 III.10. Characteristics of neural networks .................................................................. 42 III.11. Software .......................................................................................................... 43 Chapter IV. Neural Network Modelling for Spectrum Analysis ........................................................... 44 IV.1. Introduction ..................................................................................................... 44 IV.2. Data Acquisition and Preprocessing ............................................................... 44 IV.2.1. Gamma-ray Spectra Sources .................................................................................... 44 IV.2.2. Data Collection and Calibration ................................................................................. 45 IV.2.3. Preprocessing Techniques ........................................................................................ 46 IV.3. Artificial Neural Network Architecture for NaI(Tl) to HPGe-like Spectra Conversion 47 IV.3.1. Network Design (Input, Hidden Layers, Output)........................................................ 47 IV.3.2. Activation Functions and Training Parameters ......................................................... 48 IV.4. Data Preparation ............................................................................................. 51 IV.4.1. Reading Data ............................................................................................................. 51 IV.4.2. Normalizing Spect ..................................................................................................... 51 IV.4.3. Visualizing Spectra .................................................................................................... 52 IV.5. Model Preparation........................................................................................... 52 IV.5.1. Data Truncation ......................................................................................................... 52 IV.5.2. Feature and Target Matrices ..................................................................................... 53 IV.5.3. Splitting the Data ....................................................................................................... 53 IV.6. Neural Network Model ................................................................................... 53 IV.6.1. Model Design............................................................................................................. 53 IV.7. Model Compilation ......................................................................................... 54 IV.7.1. Callbacks ................................................................................................................... 54 IV.8. Model Training ............................................................................................... 54 IV.9. Model Evaluation ............................................................................................ 54 IV.10. Model Performance......................................................................................... 55 IV.10.1. Training and Validation Loss: ............................................................................... 55 IV.10.2. Test Loss and Accuracy ....................................................................................... 55 IV.10.3. Spectra Comparison ............................................................................................. 56 IV.11. Discussion ....................................................................................................... 58 IV.11.1. Model Efficacy ...................................................................................................... 58 IV.11.2. Potential Improvements ........................................................................................ 58 IV.12. Conclusion ...................................................................................................... 58 Conclusion............................................................................................................................................ 59 Bibliography ......................................................................................................................................... 61 Côte titre : MAPH/0654

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAPH/0654	MAPH/0654	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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