Physical Sciences and Mathematics Commons^™

Effective Drag Coefficient Prediction On Single-View 2d Images Of Snowflakes, Cameron Hudson

Graduate College Dissertations and Theses

The drag coefficient of snowflakes is an crucial particle descriptor that can quantify the relationships with the mass, shape, size, and fall speed of snowflake particles. Previous studies has relied on estimating and improving empirical correlations for the drag coefficient of particles, utilizing 3D images from the Multi-Angled Snowflake Camera Database (MASCDB) to estimate snowflake properties such as mass, geometry, shape classification, and rimming degree. However, predictions of the drag coefficient with single-view 2D images of snowflakes has proven to be a challenging problem, primarily due to the lack of data and time-consuming, expensive methods used to estimate snowflake shape …

Generative Adversarial Game With Tailored Quantum Feature Maps For Enhanced Classification, Anais Sandra Nguemto Guiawa

Doctoral Dissertations

In the burgeoning field of quantum machine learning, the fusion of quantum computing and machine learning methodologies has sparked immense interest, particularly with the emergence of noisy intermediate-scale quantum (NISQ) devices. These devices hold the promise of achieving quantum advantage, but they grapple with limitations like constrained qubit counts, limited connectivity, operational noise, and a restricted set of operations. These challenges necessitate a strategic and deliberate approach to crafting effective quantum machine learning algorithms.

This dissertation revolves around an exploration of these challenges, presenting innovative strategies that tailor quantum algorithms and processes to seamlessly integrate with commercial quantum platforms. A …

The Search For Heavily Obscured Active Galactic Nuclei In The Local Universe, Ross Silver

All Dissertations

Active galactic nuclei (AGN) are supermassive black holes (SMBHs) in the center of galaxies that accrete surrounding gas and emit across the entire electromagnetic spectrum. They are the most energetic persistent emitters in the Universe, capable of outshining their host galaxies despite their emission originating from a region smaller than our Solar System. AGN were some of the first sources discovered that helped teach us that there were galaxies outside of our own, and they proved the existence of black holes. Moreover, AGN can give us valuable insights into other branches of astrophysics. For example, they can be used to …

Revealing The Three-Dimensional Magnetic Texture With Machine Learning Models, Shihua Zhao

Dissertations, Theses, and Capstone Projects

Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.

We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, …

Symbolic Computation Of Squared Amplitudes In High Energy Physics With Machine Learning, Abdulhakim Alnuqaydan

Theses and Dissertations--Physics and Astronomy

The calculation of particle interaction squared amplitudes is a key step in the calculation of cross sections in high-energy physics. These complex calculations are currently performed using domain-specific symbolic algebra tools, where the computational time escalates rapidly with an increase in the number of loops and final state particles. This dissertation introduces an innovative approach: employing a transformer-based sequence-to-sequence model capable of accurately predicting squared amplitudes of Standard Model processes up to one-loop order when trained on symbolic sequence pairs. The primary objective of this work is to significantly reduce the computational time and, more importantly, develop a model that …

Background Discrimination Of A Neutrino Detector With Dense Neural Networks, Perry Siehien

Dissertations and Theses

Neutrinos are subatomic particles that weakly interact with matter due to their neutral charge and small cross section. Detectors that search for neutrinos require sensitive instrumentation, which makes them susceptible to various background sources such as gamma rays. Additionally, coherent elastic neutrino-nucleus scattering events, or CEvNS, are the weakest neutrino interactions at 1-25 keV, making them exceptionally difficult to observe. To understand the physics of CEvNS events within the detector material, the recoil signatures of relevant interactions must be determined. Traditional analysis methods are effective, but cannot be applied to energies below 50 keV, due to the overlap of discrimination …

Chasing Transients: Constructing Local Galaxy Catalogs For Electromagnetic Follow-Up Of Gravitational Wave Events, Chaoran Zhang

Theses and Dissertations

Gravitational waves (GWs) provide a new window for observing the universe which is not possible using traditional electromagnetic (EM) wave astronomy. The coalescence of compact object binaries, such as black holes (BHs) and neutron stars (NSs) generates “loud" GW signals that are detectable by the LIGO-Virgo-KAGRA (LVK) GW Observa- tory. If the binary contains at least one NS, there is a possibility that an observable EM counterpart will be launched during and/or after the merger. The first joint detection of GW radiation (GW170817) and its EM counterpart (AT 2017gfo) greatly extended our understanding of the universe in many fields, such …

Oil Particle Analysis Using Machine Learning And Holography Imaging, Daniel Cruz

Open Access Theses & Dissertations

Holographic cameras show potential as a sensor to monitor oil spills. Holographic cameras record the light interference from particles in a volume of space, producing an image called a hologram. Processing these holograms is known as hologram reconstruction. It produces a representation of particles located in three-dimensional space. These cameras can record precise shapes and sizes of particles in a volume of water. However, it is very time-consuming and resource-intensive to process the images. Most algorithms that perform particle analysis require the hologram reconstruction step. The well-documented hybrid method is one such algorithm. Machine learning is one possible technique that …

Development Of Graphical Models And Statistical Physics Motivated Approaches To Genomic Investigations, Yashwanth Lagisetty

Dissertations & Theses (Open Access)

Identifying genes involved in disease pathology has been a goal of genomic research since the early days of the field. However, as technology improves and the body of research grows, we are faced with more questions than answers. Among these is the pressing matter of our incomplete understanding of the genetic underpinnings of complex diseases. Many hypotheses offer explanations as to why direct and independent analyses of variants, as done in genome-wide association studies (GWAS), may not fully elucidate disease genetics. These range from pointing out flaws in statistical testing to invoking the complex dynamics of epigenetic processes. In the …

Third-Integer Resonant Extraction Regulation System For Mu2e, Aakaash Narayanan

Graduate Research Theses & Dissertations

A third-integer resonant slow extraction system is being developed for Fermilab's Delivery Ring to deliver protons to the upcoming Mu2e experiment. The timescale of the extraction (or spill) duration is 43 milliseconds, which is extremely short and unprecedented. Additionally, the experiment's strict and challenging requirements on the quality of the spill at this time scale has led to the development of a new Spill Regulation System (SRS) design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content suppressor. Contributions to the first two components of the SRS, i.e., Slow Regulation and Fast Regulation subsystems, …

Searching For Anomalous Extensive Air Showers Using The Pierre Auger Observatory Fluorescence Detector, Andrew Puyleart

Dissertations, Master's Theses and Master's Reports

Anomalous extensive air showers have yet to be detected by cosmic ray observatories. Fluorescence detectors provide a way to view the air showers created by cosmic rays with primary energies reaching up to hundreds of EeV . The resulting air showers produced by these highly energetic collisions can contain features that deviate from average air showers. Detection of these anomalous events may provide information into unknown regions of particle physics, and place constraints on cross-sectional interaction lengths of protons. In this dissertation, I propose measurements of extensive air shower profiles that are used in a machine learning pipeline to distinguish …

From Evaluating The Performance Of Approximations In Density Functional Theory To A Machine Learning Design, Pedram Tavazohi

Graduate Theses, Dissertations, and Problem Reports

Density-functional theory (DFT) has gained popularity because of its ability to predict the properties of a large group of materials a priori. Even though DFT is exact, there are inaccuracies introduced into the theory due to the approximations in the exchange-correlation (XC) functionals. Over the 50 years of its existence, scientists have tried to improve the design of the XC functionals. The errors introduced by these functionals are not consistent across all types of solid-state materials. In this project, a high throughput framework was utilized to compare the theoretical DFT predictions with the experimental results available in the Inorganic Crystal …

Carbon And Other Low-Z Materials Under Extreme Conditions, Jonathan T. Willman

USF Tampa Graduate Theses and Dissertations

This work is focused on understanding material's behavior and response to extreme conditions. Under extreme conditions, which is categorized as regions of high pressures and temperatures in (P-T) space, materials can undergo multiple types of phase transitions as well as exhibit substantial damage as well as other exotic behaviors. By studying matter at these extreme conditions, we can elucidate a broad range of fundamental physics including a material's energetic, mechanical, and electronic responses. This thesis describes work that makes contributions to the growing body of knowledge within these subsets of condensed matter physics. In the first thrust, crystal structure prediction …

Pattern Formation And Phase Transition Of Connectivity In Two Dimensions, Arman Mohseni Kabir

Doctoral Dissertations

This dissertation is devoted to the study and analysis of different types of emergent behavior in physical systems. Emergence is a phenomenon that has fascinated researchers from various fields of science and engineering. From the emergence of global pandemics to the formation of reaction-diffusion patterns, the main feature that connects all these diverse systems is the appearance of a complex global structure as a result of collective interactions of simple underlying components. This dissertation will focus on two types of emergence in physical systems: emergence of long-range connectivity in networks and emergence and analysis of complex patterns. The most prominent …

Quantitative Magnetic Resonance Imaging For The Early Prediction Of Treatment Response In Triple Negative Breast Cancer, Benjamin C. Musall

Dissertations & Theses (Open Access)

Triple Negative Breast Cancer (TNBC) is an aggressive subtype of breast cancer which lacks upregulated hormone receptors. Because of this, it is not vulnerable to clinically available targeted therapies. When treated with standard of care neoadjuvant systemic therapy (NAST), TNBC only shows approximately a 40% rate of pathologic complete response (pCR). A biomarker which could predict TNBC response to NAST early during treatment would be useful, as it would allow for non-responders to be triaged to alternative therapies and potentially allow for the treatment of responders to be de-escalated.

Quantitative Magnetic Resonance Imaging (MRI) may be used to probe and …

Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman

Master's Theses

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not …

Smart Quantum Technologies Using Photons, Narayan Bhusal

LSU Doctoral Dissertations

The technologies utilizing quantum states of light have been in the spotlight for the last two decades. In this regard, quantum metrology, quantum imaging, quantum-optical communication are some of the important applications that exploit fascinating quantum properties like quantum superposition, quantum correlations, and nonclassical photon statistics. However, the state-of-art technologies operating at the single-photon level are not robust enough to truly realize a reliable quantum-photonic technology.

In Chapter 1, I present a historical account of photon-based technologies. Furthermore, I discuss recent efforts and encouraging developments in the field of quantum-photonic technologies, and major challenges for the experimental realization of reliable …

A Compact Wavelength Meter Using A Multimode Fiber, Ogbole Collins Inalegwu

Masters Theses

“Wavelength meters are very important for precision measurements of both pulses and continuous-wave optical sources. Conventional wavelength meters employ gratings, prisms, interferometers, and other wavelength-sensitive materials in their design. Here, we report a simple and compact wavelength meter based on a section of multimode fiber and a camera. The concept is to correlate the multimodal interference pattern (i.e., speckle pattern) at the end-face of a multimode fiber with the wavelength of the input lightsource. Through a series of experiments, specklegrams from the end face of a multimode fiber as captured by a charge-coupled device (CCD) camera were recorded; the images …

Improving The Data Quality In Gravitation-Wave Detectors By Mitigating Transient Noise Artifacts, Kentaro Mogushi

Doctoral Dissertations

“The existence of gravitational waves (GWs), small perturbations in spacetime produced by accelerating massive objects was first predicted in 1916 as solutions of Einstein’s Theory of General Relativity (Einstein, 1916). Detecting and analyzing GWs produced by sources allows us to probe astrophysical phenomena.

The era of GW astronomy began from the first direct detection of the coalescence of a binary black hole in 2015 by the collaboration of the advanced Laser Interferometer Gravitational-wave Observatory (LIGO) (Aasi et al., 2015) and advanced Virgo (Abbott et al., 2016a). Since 2015, LIGO-Virgo detected about 50 confident transient events of GW signals (Abbott et …

Study Of Higgs Production From H -> Zz -> 4l Channel Using Machine Learning Methods, Daniel Arthur Faia Jr.

Graduate Research Theses & Dissertations

In this thesis I will show how machine learning methods can improve on physics analysis in the H -> ZZ -> 4l channel. In particular we will explore how these methods can be used to classify Vector Boson Fusion (VBF) processes in the presence of more dominant Higgs production processes. The aim is to improve the ability to discriminate VBF Higgs boson production relative to other Higgs boson production modes. Since VBF has two quark jets in the final state, it is useful to discriminate between quark and gluon jets. We compare the effectiveness of quark gluon discrimination with machine …

The Structural Information Filtered Features Potential For Machine Learning Calculations Of Energies And Forces Of Atomic Systems., Jorge Arturo Hernandez Zeledon

Graduate Theses, Dissertations, and Problem Reports

In the last ten years, machine learning potentials have been successfully applied to the study of crystals, and molecules. However, more complex materials like clusters, macro-molecules, and glasses are out reach of current methods. The input of any machine learning system is a tensor of features (the most universal type are rank 1 tensors or vectors of features), the quality of any machine learning system is directly related to how well the feature space describes the original physical system. So far, the feature engineering process for machine learning potentials can not describe complex material. The current methods are highly inefficient …

Interdisciplinary Studies Of Complex Network And Machine Learning And Its Applications, Shaojun Luo

Dissertations, Theses, and Capstone Projects

In this dissertation, we introduce the concept of network-based statistical inference methods of two types: network structure inference and variable inference. For network structure inference, we introduce correlation matrix, graphical Lasso, network clustering and identify the influencer in the network. For variable inference, we also introduce from Bayesian network, to Random Markov Field and Ising Model, Boltzmann and Restricted Boltzmann machine and the algorithm of Belief Propagation. Last but not the least, we introduce the most widely used neural network family and its two main types: Convolutional Neural Network and Recurrent Neural Network.

In Chapter 3 we provide an example …

A Study Of Neural Networks For The Quantum Many-Body Problem, Liam B. Schramm

Senior Projects Spring 2018

One of the fundamental problems in analytically approaching the quantum many-body problem is that the amount of information needed to describe a quantum state. As the number of particles in a system grows, the amount of information needed for a full description of the system increases exponentially. A great deal of work then has gone into finding efficient approximate representations of these systems. Among the most popular techniques are Tensor Networks and Quantum Monte Carlo methods. However, one new method with a number of promising theoretical guarantees is the Neural Quantum State. This method is an adaptation of the Restricted …