Physical Sciences and Mathematics Commons^™

Photonic Monitoring Of Atmospheric Fauna, Adrien P. Genoud

Dissertations

Insects play a quintessential role in the Earth’s ecosystems and their recent decline in abundance and diversity is alarming. Monitoring their population is paramount to understand the causes of their decline, as well as to guide and evaluate the efficiency of conservation policies. Monitoring populations of flying insects is generally done using physical traps, but this method requires long and expensive laboratory analysis where each insect must be identified by qualified personnel. Lack of reliable data on insect populations is now considered a significant issue in the field of entomology, often referred to as a “data crisis” in the field. …

Pyseg: A Python Package For 2d Material Flake Localization, Segmentation, And Thickness Prediction, Diana B. Horangic

Student Research Projects

Thin materials are of interest for their extraordinary physical, mechanical, thermal, electrical, and optical properties. Monolayers and bilayers of 2D materials can be manufactured through a variety of exfoliation methods. To determine layer thickness, Raman spectroscopy or other methods like Rayleigh scattering are used. These methods are, however, slow, and they require equipment beyond an optical microscope. A Python package that automates flake identification processes was built, with access solely to RGB data from an optical microscope assumed. My package, pyseg, localizes flakes on a substrate and then makes a rough estimate of their thickness from first principles. It can …

Generation Of Phase Transitions Boundaries Via Convolutional Neural Networks, Christopher Alexis Ibarra

Open Access Theses & Dissertations

Accurate mapping of phase transitions boundaries is crucial in accurately modeling the equation of state of materials. The phase transitions can be structural (solid-solid) driven by temperature or pressure or a phase change like melting which defines the solid-liquid melt line. There exist many computational methods for evaluating the phase diagram at a particular point in temperature (T) and pressure (P). Most of these methods involve evaluation of a single (P,T) point at a time. The present work partially automates the search for phase boundaries lines utilizing a machine learning method based on convolutional neural networks and an efficient search …

Imaging Normal Fluid Flow In He Ii With Neutrons And Lasers — A New Application Of Neutron Beams For Studies Of Turbulence, Xin Wen

Doctoral Dissertations

Turbulence is ubiquitous in life —from biology to astrophysics. The best direct numeric simulations (DNS) have only been benchmarked against low resolution, time-averaged experimental configurations—partly because of limitations in computing power. With time, computing power has greatly increased, so there is need for higher quality data of turbulent flow. In this dissertation, we explore a solution that enables quantitative visualization measurement of the velocity field in liquid helium, which has the potential of breaking new ground for high Reynolds number turbulence research and model testing.

Our technique involves creation of clouds of molecular tracers using 3He-neutron absorption reaction in liquid …

Search For Triple-Proton Decay Using Machine Learning With Cuore, Douglas Adams

Theses and Dissertations

A framework to search for a triple-proton decay of ¹³⁰Te in the CUORE detector against a background of muons is presented. We use machine learning to classify different kinds of energy depositing events. We use the classification information to improve our detection or non-detection limits of a triple-proton decay process. We derive and use a methodology of combining Poisson counting statistics with supervised classification machine learning tools. Additionally, a sensitivity calculation is provided which uses the classification counting likelihood. Using our analysis technique, we achieve an lower 2σ half-life bound of 7.43×10²⁴yrs for triple-proton decay of …

The Interaction Of Different Primary Producers And Physical And Chemical Dynamics Of An Urban Shallow Lake, Majid Sahin

Dissertations, Theses, and Capstone Projects

An artificial urban shallow lake, Prospect Park Lake (PPL), is situated on a terminal moraine in Brooklyn New York, and supplied with municipal water treated with ortho-phosphates. The constant input of the phosphate nutrient is the primary source of eutrophication in the lake. The numerous pools along the water course houses various aquatic phototrophs, which influence the water quality and the state of the system, driving conditions into favoring the survival of their species. In the first half of the dissertation, the focus of the project is on analyzing how the different primary producers in different regions of PPL affect …

Enabling Rapid Chemical Analysis Of Plutonium Alloys Via Machine Learning-Enhanced Atomic Spectroscopy Techniques, Ashwin P. Rao

Theses and Dissertations

Analytical atomic spectroscopy methods have the potential to provide solutions for rapid, high fidelity chemical analysis of plutonium alloys. Implementing these methods with advanced analytical techniques can help reduce the chemical analysis time needed for plutonium pit production, directly enabling the 80 pit-per-year by 2030 manufacturing goal outlined in the 2018 Nuclear Posture Review. Two commercial, handheld elemental analyzers were validated for potential in situ analysis of Pu. A handheld XRF device was able to detect gallium in a Pu surrogate matrix with a detection limit of 0.002 wt% and a mean error of 8%. A handheld LIBS device was …

Applications Of Machine Learning Algorithms In Materials Science And Bioinformatics, Mohammed Quazi

Mathematics & Statistics ETDs

The piezoelectric response has been a measure of interest in density functional theory (DFT) for micro-electromechanical systems (MEMS) since the inception of MEMS technology. Piezoelectric-based MEMS devices find wide applications in automobiles, mobile phones, healthcare devices, and silicon chips for computers, to name a few. Piezoelectric properties of doped aluminum nitride (AlN) have been under investigation in materials science for piezoelectric thin films because of its wide range of device applicability. In this research using rigorous DFT calculations, high throughput ab-initio simulations for 23 AlN alloys are generated.

This research is the first to report strong enhancements of piezoelectric properties …

An Interdisciplinary Approach To Understanding Volcanoes And Their Processes, Katherine Cosburn

Physics & Astronomy ETDs

To better understand volcanoes and their processes is important from both a fundamental science perspective and for hazard monitoring purposes. The complexity and limitations we face in pursuing such a science are numerous and this dissertation explores how an interdisciplinary approach combining physics, computer science, and volcanology can address this complexity in a straightforward and meaningful way. This is achieved through various modelling techniques across three studies: (1) a first-order analytic modelling of stratovolcano topographic shape, (2) the use of a Bayesian joint inversion on gravity and novel cosmic-ray muon measurements for imaging flat-lying subsurface density anomalies, and (3) the …

Advanced Communication And Sensing Protocols Using Twisted Light And Engineered Quantum Statistics, Michelle L. Lollie

LSU Doctoral Dissertations

Advanced performance of modern technology at a fundamental physical level is driving new innovations in communication, sensing capability, and information processing. Key to this improvement is the ability to harness the power of physical phenomena at the quantum mechanical level, where light and light-matter interactions produce technological advancement not realizable by classical means. Theoretical investigation into quantum computing, sensing capability beyond classical limits, and quantum information has prompted experimental work to bring state-of-the-art quantum systems to the forefront for commercial use. This dissertation contributes to the latter portion of the work. A set of preliminaries is included highlighting pertinent physical …

A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides

College of Sciences Posters

With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics …

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik

Faculty Publications

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …

Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski

Electrical & Computer Engineering Faculty Publications

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.

Data-Driven Methods For Low-Energy Nuclear Theory, Jordan M.R. Fox

CGU Theses & Dissertations

The term data-driven describes computational methods for numerical problem solvingwhich have been developed by the field of data science; these are at the intersection of computer science,mathematics, and statistics. When applied to a domain science like nuclear physics, especially with the goalof deepening scientific insight, data-driven methods form a core pillar of the computational science endeavor.In this dissertation I explore two problems related to theoretical nuclear physics: one in the framework of numerical statistics, and the other in the framework of machine learning. I) Historically our understanding of the structure of the atomic nucleus, the quantum many-body problem, has been …

A Surrogate Model Of Molecular Dynamics Simulations For Polar Fluids: Supervised Learning Methods For Molecular Polarization And Unsupervised Methods For Phase Classification, Zackerie W. Hjorth

Dissertations, Master's Theses and Master's Reports

Molecular Dynamic (MD) simulation is a standard computational tool in soft matter physics. While very powerful, it is computationally expensive, leading to some simulations taking days or even weeks to complete depending on the size of your computer cluster. Finding computationally cheap surrogate models which can learn the output features of MD simulation is therefore highly motivated. In this report I explore the use of deep neural network ensembles as well as support vector machine regressors as surrogate models for MD simulation. From the output of the surrogate models, we can then employ unsupervised learning methods to get insight into …

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning And Radiomics, Siqiu Wang

Theses and Dissertations

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. …

Deeply Learning Deep Inelastic Scattering Kinematics, Markus Diefenthaler, Abdullah Farhat, Andrii Verbytskyi, Yuesheng Xu

Mathematics & Statistics Faculty Publications

We study the use of deep learning techniques to reconstruct the kinematics of the neutral current deep inelastic scattering (DIS) process in electron–proton collisions. In particular, we use simulated data from the ZEUS experiment at the HERA accelerator facility, and train deep neural networks to reconstruct the kinematic variables Q² and x. Our approach is based on the information used in the classical construction methods, the measurements of the scattered lepton, and the hadronic final state in the detector, but is enhanced through correlations and patterns revealed with the simulated data sets. We show that, with the appropriate selection …

Applications Of A Combined Approach Of Kinetic Monte Carlo Simulations And Machine Learning To Model Atomic Layer Deposition (Ald) Of Metal Oxides, Emily Justus

MSU Graduate Theses

Metal-oxides such as ZnO or Al₂O₃ synthesized through Atomic Layer Deposition (ALD) have been of great research interest as the candidate materials for ultra-thin tunnel barriers. In this study, I have applied a 3D on-lattice Kinetic Monte Carlo (kMC) code developed by Timo Weckman’s group to simulate the growth mechanisms of the tunnel barrier layer and to evaluate the role of various experimentally relevant factors in the ALD processes. I have systematically studied the effect of parameters such as the chamber pressure temperature, pulse, and purge times. The database generated from the kMC simulations was subsequently used …

Establishing A Machine Learning Framework For Discovering Novel Phononic Crystal Designs, Drew Feltner

Browse all Theses and Dissertations

A phonon is a discrete unit of vibrational motion that occurs in a crystal lattice. Phonons and the frequency at which they propagate play a significant role in the thermal, optical, and electronic properties of a material. A phononic material/device is similar to a photonic material/device, except that it is fabricated to manipulate certain bands of acoustic waves instead of electromagnetic waves. Phononic materials and devices have been studied much less than their photonic analogues and as such current materials exhibit control over a smaller range of frequencies. This study aims to test the viability of machine learning, specifically neural …

Machine Learning-Driven Surrogate Models For Electrolytes, Tong Gao

Dissertations, Master's Theses and Master's Reports

We have developed a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the physical properties of ionic liquids (ILs) on lithium dendrite growth. Our simulations show that the size asymmetry between the cation and anion, the dielectric constant, and the volume fraction of ILs are critical factors to significantly suppress the dendrite growth, primarily due to substantial changes in electric-field screening. Specifically, the volume fraction of ILs has the optimal value for dendrite suppression. The present simulation method indicates potential challenges for the model extension to macroscopic systems. Therefore, we also …