Progress in addressing the simultaneous demands for increasing speed and miniaturization in electrical and computer engineering is to its greatest extent bounded by material stresses under thermal shock. Higher speeds require higher power dissipation, and smaller unit volumes make adequate power dissipation more difficult to achieve. Although there already exists a large body of research concerning endpoint thermal failure in semiconductors, there is little research available on the topic of pre-failure behavior of circuit designs containing semiconductors. Our goal is to subject a circuit containing a semiconductor-based diode to the failure mechanism of thermal shock and test the conductivity of the circuit under drastically changing ambient thermal conditions. We will then use this data to experimentally determine any observable behaviors that qualify as pre-failure symptoms. The resulting observations will be used to determine the efficacy of simulation softwares like LTspice in predicting thermal behavior of a diode circuit under extreme and rapid temperature fluctuations. Our theory is circuit simulation softwares do not account for extreme ambient thermal changes. After completing statistical analysis we will compare the experimental results to simulated results of a duplicate circuit subjected to equivalent temperature parameters and determine if we can reject our theory.

Quantum Machine Learning (QML) has shown early promise over the last few years. From simple AI algorithms to sophisticated neural networks, quantum computers have produced results that are as good as or better than their classical counterparts. However, all of these models have to deal with the memory bottleneck, which is caused by the limited number of qubits in near-term quantum devices. We instead propose a hybrid neural network that works by sandwiching any QML algorithm between two classical neural networks, using PyTorch. The design allows for an automatic scaling of quantum algorithms to inputs and outputs of any size, addressing the bottleneck issue, but it also provides an easy way of comparing classical algorithms to quantum ones and an expandability to other, more advanced classical scenarios. Additionally, the software supports the usage of configuration files, which allow for fast-paced testing of basic hypotheses, without the need of writing custom code.

The Large Hadron Collider at CERN is built to accelerate particles to high speeds and make them collide. The curved trajectory of these particles is recorded by detecting electrical charges deposited on multiple layers of tracking equipment as a particle travels through the detector. The resulting patterns are then used to reconstruct the path traveled by the particle. The more the collisions, the higher the number of charge depositions to detect. This exponential increase in data is predicted to be the main issue with the next run of the LHC experiment, where we are set to test higher energy interactions. As traditional tracking algorithms do not scale well with this increase in data, supplementing them with machine learning provides a promising solution. Scaling high-energy particle tracking in the LHC to process petabytes of data is the focus of the Exa.TrkX project, which our study is part of. In our research, we study the robustness of Exa.Trkx models and algorithms against noise and misalignment. Robustness is judged by analyzing performance metrics like purity and efficiency of pairs of charge deposits or “doublets''. Purity is defined as the ratio of true-positives over positives, and efficiency is defined as the ratio of true positives over the number of true values. A true deposit belongs to the same trajectory as the one we are comparing it with. In this presentation, I will discuss how we proved robustness against noise by observing that the change in doublet purity and efficiency was a trivial decrease of 0.3 and 0.2 percent respectively. Our research makes sure that the Exa.TrkX models can be applied to actual LHC data. We do this by proving that the models are not affected by real-life impurities in the data like noise.

The Fermilab Muon g-2 experiment seeks to measure the anomalous magnetic moment of the muon to 140 ppb. A highly purified beam of muons is delivered to a magnetic storage ring in bursts of ~15,000 muons called fills. The rate of change of the angle between a muon’s momentum and spin while orbiting in the storage ring is the anomalous precession frequency, which is directly proportional to the anomalous magnetic moment. During each fill, muons orbit in the storage ring until they decay into positrons which spiral into electromagnetic calorimeters stationed around the ring. Positrons which impact the calorimeter deposit their energy in the calorimeters as Cherenkov radiation. The time dependance of the positron energy spectrum is used to extract the anomalous precession frequency of muons in the storage ring. “Early to late effects” are a class of systematic uncertainty in the experiment which result from coherent changes of experimental conditions within each fill. These effects can directly bias the measured anomalous precession frequency. One such effect arose from malfunctioning resistors in the ring’s electrostatic quadrupoles, resulting in non-ideal vertical focusing of the muon beam. This led to coherent downward motion of the beam during each fill. This directly couples into one of the largest systematic effects, as the calorimeter acceptance depends in part on the beam's vertical position. Using data from the calorimeters, I quantified early to late change in the beam’s vertical position and vertical distribution. These results were used to cross-check results from simulation programs. If the Fermilab Muon g-2 experiment retains the same central value as the previous generation measurement but with 140 ppb precision it will be in greater than 5-sigma tension with standard model calculations. Results from Run 1 of the experiment are expected to be published in early 2021.

In recent years, as machine learning algorithms develop, particle physics have started to use machine learning algorism to solve physics problems. One of the most complicated problems is to find BSM (Physics beyond the Standard Model) signal in the standard model background. GAN (generative adversarial network) is one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process: A generator uses to generate fake data and a discriminator uses to distinguish between real data and fake data. My goal is to train a conditional GAN algorithm to generate specific fake data and apply a classifier with LHC Olympic dataset in order to find anomalies (BSM). The dataset has two regions, signal region (might have anomalies) and sideband region (only background). The final stage is to generate the fake data in the sideband region and use a binary classifier with the signal region to find if there are any anomalies in the signal region.

Many beyond the Standard Model (BSM) theories suggest the existence of more fundamental scalar fields and associated Higgs bosons than previously thought, the standard model Higgs being the lightest and most easily discovered. As independently testing each of the many heavy Higgs theories would be inefficient, in this talk, I describe the use of an alternative, model-independent, generic approach to model exclusion and validation in the search for a generic heavy Higgs boson theorized to have both 4-dimensional (dim-4) and effective 6-dimensional (dim-6) interactions with the Standard Model particles. If the generic heavy Higgs is connected with BSM physics at the scale of a few teraelectronvolts (TeV), we will see an excess beyond Standard Model predictions in several observables at high transverse momentum. I will discuss the role of the dim-4 and dim-6 operators at play, before expounding upon the simulations used to characterize generic heavy Higgs production in proton-proton collisions and the corresponding Large Hadron Collider (LHC) data. Channels, signal regions, and control regions are defined within an event data model analysis framework to maximize the significance of any potential result. In addition, cuts are made on key variables, such as the invariant mass of the hadronic W boson in the same-sign dilepton signal region and the invariant mass of the heavy Higgs in the trilepton signal region in order to separate the known physics from any potential new physics. If discovered, a generic heavy Higgs would validate a key part of many BSM models and help to focus such theoretical work, while also founding an entirely new area of research for experimentalists.

Classification of particle jets that originate from light or heavy flavor quarks is an important task in determining the nature of particles created in collisions. This data can be first preprocessed into a list of particle tracks, which then can be processed sequentially. Recurrent Neural Networks (RNNs) are a powerful tool that is used to process sequential information, and we develop several RNNs such as the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks to classify the data into background and signal jets. The transformer is also a new, powerful model that is used to process sequential information, and is said to be more powerful and more efficient to train than the LSTM. We study the performance of the transformer compared to RNN models by training the transformer model on the jet flavor dataset, and then comparing the AUC values of each respective model. We find that the transformer outperforms the LSTM models in classifying light and heavy flavor quarks, and that it does so with less training parameters.

In the ATLAS detector, high energy quarks and gluons can be produced in proton-proton collisions (p-p collisions). Quarks and gluons, elementary particles of the standard model of particle physics, have a quantity called color charge which subjects them to color confinement; only color neutral groups, called hadrons, can be observed and thus a single quark or gluon cannot be observed. This poses a challenge in determining whether a quark or a gluon was produced in a collision. Each quark or gluon produced will create a multitude of hadrons within several femtometers of the collision, which are then grouped together to form jets. This analysis uses five variables from the jet: transverse momentum, energy correlation, track multiplicity, and jet width, to differentiate quark-initiated jets and gluon-initiated jets. Previous calibrations only used track multiplicity based tagger, whereas this calibration will use a boosted decision tree based tagger. My task is to provide an analysis of the scale factor between simulation and detector data (with a desired value of 1) and systematic uncertainties. Preliminary results show that the scale factor is about 0.8-1.2 with systematics around 8%.

Accurate calibration of the Laser Interferometer Gravitational-wave Observatory(LIGO) is vital to detecting gravitational waves and interpreting astrophysical events. Gravitational wave measurements have prompted extensive studies in coalescing binary systems, early formation of the universe, and other areas of cosmology. The current calibration method relies on radiation pressure to induce a calibration force. However, a new method of calibration, the Newtonian Calibrator, uses gravitational attraction to inject a calibration force. We have developed two simulations to model this injection: a finite element analysis and multipole-based calculation. These simulations allowed for the accurate prediction of the injected force, yielding a precise absolute calibration. The accuracy of this calibration is crucial for cross-checking LIGO's current calibration techniques and making future cosmological predictions. 

The success of the Standard Model of particle physics has incentivized attempts to find new theories that go beyond the Standard Model. Computer simulations of the particle colliders and their detectors are required to evaluate the validity of these new theories for experimental research. RECAST is a framework for reinterpreting Large Hadron Collider analyses using Yadage computational workflows. RECAST-workflow builds on RECAST in order to run truth-level reinterpretations which achieve much faster results by sacrificing complexity. It also allows for workflows to be modularized through subworkflows which encapsulate each step (generation, selection, analysis). The aim of our work is to improve upon the existing integration of the event generator MadGraph to support custom models, as well as adding the additional generators Sherpa and Herwig. This is done through modifying the existing python code base and creating portable Docker containers to encapsulate the programs in each step. This tool was applied to the SVJ model for both t-channel and s-channel and the results compared to published simulations which we anticipate to match. In this talk we will demonstrate the utility of RECAST for fast and modular particle simulations, highlighting the new generators and how they can be applied to study new interesting models.

Comets are cosmic snowballs of frozen gases, rock and dust that orbit the Sun. Isaac Newton suspected that comets were the origin of the life-supporting component of air and a key source for water replenishment in planetary interiors. A close-up view of comet Hartley 2 was taken by NASA's EPOXI mission during its flyby of the comet, using the spacecraft's medium resolution instrument. Comet Hartley has a novel asymmetric dumbbell-like shape. We employed mathematical models to study comet Hartley. Using calculus-based methods, we estimated various static properties, including the arc lengths (outer boundary length), surface area, and volume of Comet Hartley. Assuming a constant density, we also estimated the mass, center of mass, and moments of inertia for Comet Hartley using triple integration methods using cylindrical coordinates. The center of mass, moments of inertia, and radius of gyration form key inputs for studying the orbital mechanics of the comet in outer space. This research project provides excellent opportunities for hands-on explorations using multivariable calculus studies for engineering and space sciences applications. This research is important as studies of comets unravel secrets about the formation of the solar system.

STS-121 is a NASA space shuttle mission to the International Space Station (ISS). The ISS is a habitable satellite (Space station) in a low Earth orbit. We employ calculus-based methods to analyze and study the flightpaths, altitude, velocity, and acceleration profiles of the STS121 data reported by NASA as it travelled through outer space. Our studies unravel information about the critical points, local maxima and minima, concavity, and inflection points in the altitude data. The velocity profiles were fitted to polynomial functions using least square data fitting using linear algebra-based methods. The acceleration data involve piecewise functions which is related to the time scales involving burning of the propellent and separation of the external propellant tank as the space shuttle gets ready to move into orbit. We estimated the work done in transferring a load from Earth to the International Space station. We used optimization methods to design an optimal solar panel geometry for a satellite by minimizing the surface area. This research provides novel applications of the fundamental theorems of calculus to study motion in outer space and involves mathematical modeling, optimization, curve fitting, data analysis and data visualization.

Gliders are robotic vehicles used in the air and underwater to collect and transmit real-time data. Studies using gliders have important applications in oceanography, engineering, and remote sensing. The goal of this project was to model and identify aspects of a glider’s flight using vector-calculus and matrix-algebra based methods. We employed mathematical models to study the flightpath of a glider using vector valued functions and calculated the osculating plane of the glider. The model parameters were optimized to minimize turbulence. We studied the kinematics of underwater gliders using GPS data reported from gliders deployed by Rutgers University and the University of Washington. We analyzed the reported glider velocity data and applied vector-calculus based methods to calculate the instantaneous and average velocities and acceleration vectors. Additionally, we applied matrix-algebra based methods to translate and rotate the glider to position it at appropriate coordinates underwater for gathering data. This research provided insight into mathematical modeling of real-world data and involved applied optimization and data visualization. These studies provide novel avenues for hands on exploration and application of key mathematical concepts.

In our research, we will focus on trying to explain the dynamics of certain particles known as pseudo scalar mesons. These kinds of particles are charged both under electromagnetism as well as under the strong interaction coupling. Due to the presence of these dual couplings, the particles will interact both via the strong and the electromagnetic interaction. To assess the dynamics of these kinds of interacting particles, one can either resort to using approximate lattice constructions of the full theory to calculate its properties, or construct tractable effective field theories. In this vein, this work will compare a non-relativistic effective field theory, which perturbativley takes into account the electromagnetic interaction, and reproduce such results through a lattice approximation. In the work, I find that both the effective field theory and the lattice construction are shown to be in agreement for two to three body meson interactions.

In this project, we measure electron flow in graphene, a 2-D lattice of carbon atoms, and compare the results to simulations that we run. As current is passed through typical electrical devices, electron transport is dominated by momentum relaxing electron-phonon scattering, i.e. electrons colliding with the impurities and vibrations of the crystal's lattice structure. This is typical of the omhic regime. However, other modes of electron transport are possible. In clean graphene, for example, electrons are weakly coupled to lattice sites and electron-electron scattering dominate. In these interactions, momentum transferred between electrons is conserved. When measured over a range of temperatures, we find dips in the resistance, resulting from these hydrodynamic electrons’ tendency to “pull” one another along with the bulk. Analogous to honey, these electrons have viscosity, which unlike resistivity, is a property of the fluid. This research will further elucidate properties of this electron fluid. To complete this project, we will fabricate graphene devices and study them in a table-top cryostat, measuring the current output from 4K to room temperature. We are particularly interested in how this viscous fluid behaves as it encounters a boundary within the device, an open question in the field of solid-state physics. We use a low voltage probe tip which can be positioned anywhere within the device. By blocking a portion of the drain with the probe-tip and measuring the current output along segments of the drain, we may gain insight into the boundary conditions of the electron fluid. This research will directly benefit the electronics industry: the next generation of computer chips will utilize 2-D materials such as graphene, potentially enabling the useful properties of hydrodynamic flow to be exploited.

The ATLAS detector is the largest general-purpose particle detector at the Large Hadron Collider, surrounding a site where protons collide at near-light speed and recording the resulting expulsion of particles and energy with an array of sub-detectors. Particles travelling outward from the collision deposit charge in clusters of cells (‘cluster images’) along the electromagnetic and hadronic calorimeters of the ATLAS detector. A convolutional neural network is used to analyze cluster images generated by incident Pions and classify whether the incident Pions are neutral or charged. For each type of Pion, a dense neural network is used to analyze cluster images and predict the energy of the incident Pions. In this project, I implemented a mixture density network in place of the dense neural network to analyze cluster images and predict the energy of incident Pions as well as the associated uncertainty of the energy for each cluster. The energy resolution of each cluster contains important information for the purposes of tracking particles’ trajectories throughout the detector, especially as collisions become more energetic and particles with overlapping tracks become more numerous; propagating the uncertainty from each cluster to the particle tracks would result in more accurate measurements by the ATLAS detector, allowing the standard model of physics to be studied under greater scrutiny.

From 2004 to 2018 the Centers for Disease Control and Prevention recorded an average of 702 deaths per year in the United States related to excessive heat events. With more of the world’s population now living in cities, understanding the urban heat island effect and its impact on morbidity and mortality is increasingly important. In the US, the intensity of the urban heat island effect is well known in Atlanta, GA. As part of a team, I examined the extent to which ground cover affects the minimum, maximum, and average temperatures in Atlanta compared to its surrounding neighborhoods. Temperature data reported by weather stations was gathered at varying distances from downtown, and cross-referenced to a map of the land cover in Atlanta to find that temperatures varied greatly as the distance from downtown increased. Overall, it was found that minimum temperatures varied more than average or maximum temperatures, and of all the ground cover types studied, more urbanization contributed to warmer temperatures. This study builds on these findings by introducing the coarse-grained urban heat island model constructed by Gabriele Manoli and colleagues at ETH Zurich which considers factors beyond just ground cover. By applying the Manoli, et al, computational model to Atlanta Georgia I can further examine the urban heat Island effect in that region, and explore the patterns observed in previous findings. The implications of this model stretch far beyond Atlanta Georgia to help form geographically targeted guidelines for urban centers for which extensive research has not been done to understand temperature trends.

Understanding the spread of COVID-19 is important to all aspects of our life in this pandemic. The more we know about how COVID is transferred from one person to another the more quickly we can come up with counter-measures and protective practices. One of the key ways we know that the disease spreads is on water droplets expelled as we talk and breathe. The spread of these droplets should match our understanding of the spread of an aerosol, which we here model using Computational Fluid Dynamics. We use the popular CFD platform OpenFOAM to simulate the spread of aerosols in a 3D model of our physics lab room. In conjunction with the computer simulation, we construct a small scale physical model of the lab room, and with the help of a high speed camera and fluorescent dye, we track the actual spread of water droplets expelled into the enclosed space. These comparative experiments help us to understand where the simulated model needs refinement and provide valuable insights into how we can combat the spread of this pandemic.

The COVID-19 shutdown in the states of Washington and New York significantly reduced transportation and cut normal daily activities due to constraints issued by governments. To understand how air pollution was affected during the shutdown, this research studied various air pollutants at two different locations in each state; Seattle and Olympia/Tacoma in Washington state, and New York City and Rochester in the state of New York. Daily averages of carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, PM2.5, and tropospheric ozone were collected for each location from 2016-2020, including the months from January through August. A linear regression model with a 95% confidence interval, was built using the 2016 to 2019 data to estimate the monthly averages for 2020 to determine if there was a change in any of the air pollution levels due to the COVID-19 shutdown. While there was no notable difference in most of the air pollution levels during the COVID-19 shutdown, there was a significant drop in nitrogen dioxide levels at all four locations. More surprisingly, carbon dioxide was showing an increase during the shutdown. It is speculated that there are two reasons behind the increase in carbon dioxide. First, carbon dioxide is showing an overall yearly increase during our selected research time interval. Secondly, the biggest carbon dioxide producers are industry and power plants. Due to said constraints and confinements, it is concluded that households' electricity consumption went up. This could be explained by the fact that schools and businesses moved entirely online requiring everyone to participate via video conferencing systems and to operate daily tasks via online platforms. As a whole, this research is significant to the study of climate change and its effects, and mitigation of said effects of climate change.

Electrons bound to donors (impurities in a crystal) in the semiconductor zinc oxide (ZnO) are promising candidates for solid-state spin qubits. These qubits may be useful for building quantum memories, which are necessary for establishing long range quantum communication. To actually use these electrons as qubits, we have to understand transitions from the donor-bound exciton state (where there are 2 electrons and a hole bound to the impurity) to the neutral donor state (with just a single bound electron). The width of the distribution of photon wavelengths emitted by this transition (where an electron and hole recombine) is the excitonic linewidth. This linewidth will determine our ability to store quantum information, since entanglement requires close to identical photons. The linewidth can be affected by isotopic randomness in the crystal, an effect which may be pronounced in ZnO due to wide distribution of isotopes in zinc. Studying this effect can tell us if isotopically pure ZnO is required for building quantum memories. This work will present results from theoretical models created in Python and Mathematica simulating the influence of isotopic randomness on the observed linewidths, and will compare these estimates with experimental data. These models simulate particles bound to impurities in isotopically varied crystal environments, determine their wavefunctions and the effect of isotope on their energies, and estimate the resulting linewidth. This theoretical estimate will be compared to experimental data obtained by photoluminescence excitation spectroscopy. The current experimental linewidth, measured by the full width at half maximum (FWHM) is 46 μeV operating at a temperature of 1.53 K. Initial models predict relatively high values, from 25 – 45 μeV FWHM. Results from this model and a more refined model focused on the neutral donor state’s wavefunction will be discussed.