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.

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%.

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.

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.

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.