Physical Sciences and Mathematics Commons^™

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 …