Engineering Physics Commons^™

Physics Alumni Newsletter Spring 2026, Terry Goforth

Physics Alumni Newsletter

Physics Alumni Newsletter

The Physics Alumni Newsletter is produced by the SWOSU Physics Department.

Our Engineering Physics students are recruited in fields such as electronics, aerospace, mechanical engineering, petroleum engineering and software engineering. Graduates also have careers in meteorology, architecture, education and more.

Guitar Amplifier Directivity, Rachel C. Edelman, Brian E. Anderson, Samuel D. Bellows, Timothy W. Leishman

Directivity

No abstract provided.

In Situ Observations Of Thermal Ions In Perturbed Ionospheres: Techniques And Results, Magdalina Louise Moses

Dartmouth College Ph.D Dissertations

Prediction and mitigation of space weather events are active research topics that require knowledge of the physics governing the ionosphere. Sounding rockets can be used to make in situ observations. The Lynch Rocket Lab created the Petite-Ion-Probe (PIP), a small retarding potential analyzer, to measure thermal ion parameters (i.e., ion density and temperature). A PIP's raw data consists of a series of measured anode currents as a function of screen bias voltages, called IV curves. PIPs can be integrated onto a sounding rocket’s main payload and/or be deployed from the rocket on small platforms called ``PIP-Bobs''. Note that as the …

Explainable Physics-Based Constraints On Reinforcement Learning For Accelerator Optimization, Jonathan Colen, Malachi Schram, Kishansingh Rajput, Armen Kasparian

Data Science Faculty Publications

We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at …

Utilizing Computer Modeling To Optimize Electric Fields Within Xenon Time Projection Chambers, Miles Meloni

Honors Theses

XENONnT is a physics experiment designed with the goal of detecting dark matter particles. The detector is a time projection chamber; a series of charged electrodes creates an electric field, surrounding a central body filled with liquid and gaseous xenon. Photomultiplier tubes (PMTs), positioned on either end of the chamber, serve to detect light signals. We seek to minimize the root mean square of the electric field norms experienced by the PMTs. This quantity corresponds to the variance in the electric field observed by the PMTs. Establishing a consistent electric field is important to maintaining these sensitive components. The electric …

Preliminary Observations From The Relativistic Electron Atmospheric Loss (Real) Satellite Mission, Evzen Selvon, Robyn Millan

Wetterhahn Science Symposium Posters

The Relativistic Electron Atmospheric Loss (REAL) spacecraft (launched in July 2025) is a 3U cubesat designed to measure the precise energies (1 keV – 2MeV) and pitch angles of electrons entering the Earth’s ionosphere. The mission involves Dartmouth, BU, JHUAPL, MSU, and NASA. REAL carries three particle sensors measuring low, medium, and high energies. This work is focused on the ElectroStatic Analyzer (ESA) instrument, designed to measure lower energy electrons (1-40 keV) in the directions parallel and perpendicular to the Earth’s magnetic field. This research aims to identify notable events observed by the REAL spacecraft for future analysis.

Enhanced Hydrophilicity And Self-Cleaning Properties Of Tio₂ Thin Films On Glass For Photovoltaic Applications: Influence Of Precursor Concentration And Light Irradiation, Fatma Refaat, Hany Hashem, Mohamed Mahmoud Gouda, Ibrahim Ahmed, Khaled Abdelwahed, Ahmed Bakr El Basaty

Trends in advanced sciences and technology

This study investigates the modification of glass substrates with TiO₂ thin films to enhance surface hydrophilicity and self-cleaning behavior. The films were deposited on glass by spin coating using different precursor concentrations, followed by thermal treatment. Structural and optical properties were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and UV–Vis spectroscopy, while surface wettability was evaluated through water contact angle measurements. XRD confirmed the formation of pure anatase TiO₂, while FTIR spectra verified the presence of Ti–O and Ti–O–Ti bonds. Wettability analysis showed that films prepared with the lowest precursor concentration exhibited the …

The Shms 11 Gev/C Spectrometer In Hall C At Jefferson Lab, S. Ali, A. Ahmidouch, G. R. Ambrose, A. Asaturyan, C. Ayerbe Gayoso, J. Benesch, V. Berdnikov, H. Bhatt, D. Bhetuwal, D. Biswas, P. Brindza, M. Bukhari, M. Burton, R. Carlini, M. Carmignotto, M. E. Christy, C. Cotton, J. Crafts, D. Day, S. Danagoulian, A. Dittmann, D. H. Dongwi, B. Duran, D. Dutta, R. Ent, H. Fenker, M. Fowler, D. Gaskell, A. Hamdi, N. Heinrich, W. Henry, N. Hlavin, T. Horn, G. M. Huber, Y. Ilieva, J. Jarrell, S. Jia, M. K. Jones, M. Junaid, M. L. Kabir, N. Kalantarians, A. Karki, S. J.D. Kay, C. E. Keppel, V. Kumar, S. Lassiter, W. B. Li, D. Mack, S. Malace, J. Mcmahan, A. Mkrtchyan, H. Mkrtchyan, P. Monaghan, C. Morean, P. Nadel-Turonski, G. Niculescu, M. I. Niculescu, A. Nadeeshani, E. Pooser, A. Ramos, J. Reinhold, B. Sawatzky, H. Szumila-Vance, V. Tadevosyan, R. L. Trotta, A. Usman, C. Yero, M. Yurov, S. Zhamkochyan, S. A. Wood, J. Zhang

Physics Faculty Publications

The Super High Momentum Spectrometer (SHMS) has been built for Hall C at the Thomas Jefferson National Accelerator Facility (Jefferson Lab). With a momentum capability reaching 11 GeV/c, the SHMS provides measurements of charged particles produced in electron-scattering experiments using the maximum available beam energy from the upgraded Jefferson Lab accelerator. The SHMS is an ion-optics magnetic spectrometer comprised of a series of new superconducting magnets which transport charged particles through an array of triggering, tracking, and particle-identification detectors that measure momentum, energy, angle and position in order to allow kinematic reconstruction of the events back to their origin at …

The Xpdirc Concept For Next-Generation Dirc Detectors, R. Dzhygadlo, J. Datta, K. Dehmelt, A. Deshpande, T. K. Hemmick, Md. I. Hossain, C. E. Hyde, Y. Ilieva, G. Kalicy, W. J. Llope, P. Nadel-Turonski, C. Schwarz, J. Schwiening, N. Shankman, J. Stevens, N. Wickramaarachchi, C. Woody

Physics Faculty Publications

The next-generation DIRC (xpDIRC) represents a novel detector geometry concept currently being developed for advanced particle identification systems in high-energy physics experiments. Building upon the high-performance DIRC (hpDIRC) designed for the ePIC detector at the Electron-Ion Collider, the xpDIRC introduces a hybrid optical architecture that combines enhanced focusing optics, a wide plate light guide, and a compact expansion volume. Comprehensive Geant4 simulations demonstrate that the xpDIRC hybrid geometry achieves state-of-the-art performance in π/K separation across the entire range of operation.

K-Long Facility At Jlab, Moskov Amaryan

Physics Faculty Publications

In this talk I present the outline of K-long Facility (KLF) at JLab [1]. It was approved by PAC48 in 2020 to run for 200 days of beamtime, equally divided between liquid hydrogen and deuterium targets, to measure dozens of hyperon states predicted by CQM and LQCD but not yet established. This facility also will allow to measure Kπ scattering in different channels to observe the so-called ᴷ scalar meson and measure its width and position with unprecedented accuracy. Finally, it will be shown that exotic baryons can be measured at this facility in formation reactions with a two-body final …

Geometry Of Almost-Conserved Quantities In Symplectic Maps: Approximate Invariants In Nonlinear Accelerator Systems, T. Zolkin, S. Nagaitsev, I. Morozov, S. Kladov

Physics Faculty Publications

We present a perturbative method for constructing approximate invariants of motion directly from the equations of discrete-time symplectic systems. This framework offers a natural nonlinear extension of the classic Courant-Snyder (CS) theory for systems with 1 degree of freedom—a foundational cornerstone in accelerator physics now spanning seven decades and historically focused on linear phenomena. The original CS formalism emerged under conditions where nonlinearities were weak, design goals favored linear motion, and analytical tools—such as the Kolmogorov-Arnold-Moser theory—had not yet been fully developed. While various normal-form methods have been proposed to treat near-integrable dynamics, the approach introduced here stands out for …

Ai-Enabled Digital Twins And Optimization Workflows For Accelerator Control, M. Yadav, A. Seryi, B. Terzic, J. Bird, J. Delayen, K. Makino, K. Ahmed, L. Van Riesen-Haupt, Q. Su, S. De Silva, S. Hossain, T. Griffin, T. Satogata

Physics Faculty Publications

We propose to develop advanced ML models, such as physics informed neural network (PINN) based surrogate models, to accurately represent accelerator phase space transport. These surrogate models will enable precise diagnosis and prediction of beam phase space evolution along the beamline, facilitating real-time control and optimization. The developed models will be tested using the Upgraded Injector Test Facility (UITF) at Thomas Jefferson National Accelerator Facility (JLab), providing a pathway toward ML-driven enhanced diagnostics and beamline control in operational accelerator environments. The primary aim will be to facilitate this by developing machine learning models that outperform traditional simulations in speed and …

Physics-Informed Temperature Prediction Of Lithium-Ion Batteries Using Decomposition-Enhanced Lstm And Bilstm Models, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

Accurately forecasting the operating temperature of lithium-ion batteries (LIBs) is essential for preventing thermal runaway, extending service life, and ensuring the safe operation of electric vehicles and stationary energy-storage systems. This work introduces a unified, physics-informed, and data-driven temperature-prediction framework that integrates mathematically governed preprocessing, electrothermal decomposition, and sequential deep learning architectures. The methodology systematically applies the governing relations to convert raw temperature measurements into trend, seasonal, and residual components, thereby isolating long-term thermal accumulation, reversible entropy-driven oscillations, and irreversible resistive heating. These physically interpretable signatures serve as structured inputs to machine learning and deep learning models trained on temporally …

Mtl_Tx: A Multi-Task Transformer Model For Improved Radiation Time-Series Estimation, Hongfang Zhang, Adam Stavola, Hal Ferguson, Bence Budavari, Hongyi Wu, Chiman Kwan, Jiang Li

Electrical & Computer Engineering Faculty Publications

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed …

Variational Autoencoder Inverse Mapper For Extraction Of Compton Form Factors: Benchmarks And Conditional Learning, Douglas Adams, Md Fayaz Bin Hossen, Joshua Bautista, Gia-Wei Chern, Simonetta Liuti, Marie Boër, Marija Čuić, Michael Engelhardt, Gary R. Goldstein, Huey-Wen Lin, Yaohang Li

Computer Science Faculty Publications

Deeply virtual exclusive scattering processes (DVES) serve as precise probes of nucleon quark and gluon distributions in coordinate space. These distributions are derived from generalized parton distributions (GPDs) via Fourier transform relative to proton momentum transfer. QCD factorization theorems enable DVES to be parameterized by Compton form factors (CFFs), which are convolutions of GPDs with perturbatively calculable kernels. Accurate extraction of CFFs from DVCS, benefiting from interference with the Bethe–Heitler (BH) process and a simpler final state structure, is essential for inferring GPDs. This paper focuses on extracting CFFs from DVCS data using a variational autoencoder inverse mapper (VAIM) and …

A Survey On Generative Ai For Detector Effects Unfolding In Particle And Nuclear Physics, Tareq Alghamdi, Tommaso Vittorini, Jitao Xu, Marco Battaglieri, Derek I. Glazier, Glòria Montaña, Giorgio Foti, Alessandro Pilloni, Nobuo Sato, Yaohang Li

Computer Science Faculty Publications

In particle and nuclear physics, “detector effects unfolding” can be viewed as a highdimensional inverse problem whose goal is to recover the true event distributions from observed experimental data corrupted by detector-induced distortions. Recent advances in generative AI have positioned data-driven and machine learning-based approaches as powerful alternatives to traditional unfolding techniques, offering superior scalability to high-dimensional data, capability of learning complex detector responses, and the ability to operate directly at the event level. We survey state-of the-art generative AI-based models for detector folding and unfolding. We review existing architectures and training strategies, and highlight recent methodological advances and open …