Session CC03: V: Undergraduate Research

Extending a User Interface Class for the KinFit Library

In particle physics, kinematic fitting is a technique used to improve the resolution of experimental measurements. This is done using conservation laws and different constraints, such as mass or momentum constraints. One widely used constraint is the 3-constraint (combination of geometric and kinematic constraints) which is implemented in the KinFit library that is used in the HADES experiment. Within KinFit is a user interface class that aims to ease the process of using the library as compared to using all of the library’s individual classes in an analysis, which can be very cumbersome and error prone. The class contains functions to apply constraints for various fits, and the goal is to add a function to enable the UI to successfully do 3C fits. The fit quality obtained using the method was compared to the quality of a manual fit without the UI class. The constraint was applied by generating an undetected candidate using known decay products of this particle and other particles in the event. The class was modified, and the fit was then applied. The results such as the χ2 and probabilities and comparison to the manual fit will be presented. The UI should be further modified to also include pull distributions to better know the quality of the fit, as well to make the UI more accessible.   

Moment of inertia tensors of non-polarized nucleons using a parton model

Neutron star formation is governed by the β-decay nuclear reaction that transforms protons and electrons to neutrons and neutrinos. This study describes a classical model that estimates the Gibbs free energy associated with this phenomenon from the equilibrium constant of the nuclear reaction (K). Previous work based on this idea had assumed a three quark model for the proton and neutron and calculated K by finding the moment of inertia of a rigid asymmetric top for each nucleon.

We build on that method by creating a Jupyter Notebook-based model with the assumption that the proton and neutron’s charges and masses can be modeled as a set number of point-like partons, attributed to Feynman’s Parton Model. Their respective quarks were also considered to be made up of partons. Models were created using known overall mass and charge vs. attributing it to the particular up and down quarks. We used the charge distributions to generate three-dimensional data frames of random points for each nucleon. These were then assigned to a “shell” in the spherical probability distribution we created manually. Finally, we calculated the moment of inertia of each nucleon using the definition for a symmetric spherical body and found K from the ratio of the neutron to the proton. From K, we obtained the Gibbs free energy of the nuclear reaction and compared it to the known estimated energy of a supernova explosion during core collapse. Results were very close to the maximum recorded output in the literature: 10⁴⁶ J.    

Modeling Polytropic Stars in General Relativity with a Cosmological Constant

Spherically symmetric compact objects are one of the most interesting solutions in General Relativity. In this project, we analyze solutions with a generalized polytropic equation of state and a cosmological constant. We derive a generalized Tolman-Oppenheimer-Volkoff equation, using which we convert the Einstein field equations into a differential equation for the mass profile of the star. This equation is highly nonlinear and it is impractical to solve it analytically. We find a few simple numerical solutions corresponding to particular values of polytropic index while analyzing the physical acceptability and the spacetime properties of solutions.   

Energy Dependent Morphology of HAWCJ2019+368, a Energetic Pulsar in the Dragonfly Nebula

Inside the Dragonfly nebula is the pulsar PSR J2021+3651, one of the brightest sources of TeV gamma rays. Data from the High Altitude Water Cherenkov (HAWC) observatory has resolved the MGRO J2019+37 region, where the pulsar and nebula are located, into two sources: HAWC J2019+368 and HAWC J2016+371. The study of this source’s energy dependent morphology is indicative of the underling particles causing this gamma ray emission. Energy dependent morphology indicates that the size of the source is limited by energy lost in the particles producing gamma-rays. Using an expanded dataset from HAWC, we will search for energy dependent morphology. Measuring this energy dependent morphology will confirm the interpretation of HAWC J2019+368 as a TeV Pulsar Wind Nebula (PWN).   

Progress Update on the JSNS^2 Experiment

In this presentation, I will introduce the JSNS^2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) experiment, including its goals, methods, and current results. The driving goal behind this experiment is the search for neutrino oscillation involving sterile neutrinos using a source of muon antineutrinos from muon decay at rest, which then are detected using gadolinium loaded liquid scintillator. My own work has focused on the kaon decay at rest (KDAR) measurement. Within this session, I will go over specific problems that we have faced (counteracting background events like cosmic muons, oversaturated PMTs, etc.) as well as an update on the measurement progress.   

Visualization of Rosenbluth Separations

The electromagnetic form factors of the proton can be determined by making measurements at the same four momentum transfer, Q², but with different beam energies.   This is a technique known as a Rosenbluth separation.   The results of this type of measurement are typically plotted as the reduced cross-section against the values of epsilon, a value calculated from the beam energy and scattering angle, and fitting a line to the data; but this method makes it very hard to see the physical quantities of interest from such a plot.   Back in the late 60’s, Nobel laureate Robert Hofstadter proposed a different way to visual Rosenbluth data.  In this talk we will use classic SLAC elastic scattering data to show these two different ways of presenting the data and illustrate the value of Hofstadter’s visualization technique.