2020 TUNL REU Projects

Nuclear Physics Projects

1. Data Evaluation Activities at TUNL
Advisor: John Kelley

Student: Beshoi Grees

The nuclear data group at TUNL compiles, evaluates and disseminates nuclear structure data relevant to A=2-20 nuclides. Our activities primarily involve surveying literature articles and producing recommended values for inclusion into various US Nuclear Data Program databases. We have projects related to compiling structure data from recently published articles, and producing full nuclear structure data evaluations of nuclides based on all existing literature. An involved student could select activities based on their interests.

2. Analysis of Activation Data for Short-Lived Radioisotopes
Advisor: Krishichayan
Student: Olivia Dickinson

Basic nuclear reaction data, such as (n,2n), (n,g), and (g,n) on short-lived isotopes are critically important to a breadth of scientific fields.  The activation technique is one of the direct techniques for such cross-section measurements but is limited to the isotopes having long half-lives (i.e. hours and longer). With the availability of a fast transfer system, namely, RABITTS at TUNL, the activation technique can be used to study such reaction cross-section for nuclei having half-lives between seconds and minutes.

This virtual project will be focused on the conceptual and computational work on the study of the potential isotopes produced using neutron and photon as an incident probe. This includes a literature survey, calculating and estimating count rates of specific nuclear reaction channels, and analysis of some old data.

3. Stellar Modeling for Nuclear Astrophysics with MESA
Advisors:  Art Champagne and Amber Lauer
Student:  Ian Lapinski

Stellar modeling is an important resource to guide and inform Nuclear-Astrophysical experimental efforts. These models seek to replicate a specific stellar environment with an emphasis on nuclear reaction networks. The models are then run, sometimes hundreds of times, varying the rate of a single reaction each time, to test its impact on various features of the environment. Stellar modeling is especially important to exotic environments, as these present the most barriers to experiment, theory, and observation. Black holes, Asymptotic Giant Branch stars, and Low-Mass X-ray Binaries are locations of some of the important reactions being studied today. Due to intense gravity, lack of nuclear EOS, and unknown reaction rates there are many uncertainties in these environments and useful stellar models are still under development. In order to explain the various features of these environments, we are interested in studying the effects of alterations to the nuclear network via the aforementioned methods. The student will gain familiarity with Modules for Experimental Stellar Astrophysics (MESA http://mesa.sourceforge.net/index.html) and the Fortran programming language to assist in developing or improving on existing models to provide a useful model for nuclear astrophysics study. Additionally, the student may design scripts via python, Mathematica, or another agreed upon language, that will parse the results of stellar evolution models into usable data and visualizations that will summarize the results.

4. Developing Ray-Tracing Techniques for High-Resolution Charged Particle Spectroscopy
Advisor:  Richard Longland
Student:  Briana Strickland

Experimental nuclear astrophysics involves performing nuclear reaction measurements in the laboratory to understand how stars burn their fuel. Often, the reactions that happen in stars are inaccessible on Earth. They may involve short-lived radioisotopes (so target material cannot be made), or their rate might be too slow to feasibly measure. To solve these issues, we turn to novel nuclear structure techniques, in particular particle transfer reactions. To measure a particle transfer reaction, we accelerate a beam of charged particles to high energy, which are then focused on a thin target. In the target, the beam particles will undergo nuclear reactions. By analyzing the products of those reactions, we can learn a great deal of nuclear structure information, and thus, the reactions occurring in stars.

This project includes aspects of every part of our charged-particle spectroscopy program. Our detector will be upgraded to enable particle ray-tracing as they traverse the detector. That capability will enable us to sharpen our image of the reaction products. An analysis code will be developed to perform that sharpening procedure, which will be fully characterized with detector simulations. Once the system is in place, we will perform an experiment with the upgraded charged particle spectrograph to calibrate and quantify the improvements this technique provides to our experimental program. The student will work closely with Dr. Longland on this project, but will also interact with graduate students in our group who have detector development and computational expertise. They will also join our larger nuclear astrophysics group, where there are over a dozen researchers from NC State and UNC.

5. Neutrino Physics Studies
Advisor: Kate Scholberg
Student: Loida Rosado Del Rio

Coherent neutral current neutrino-nucleus elastic scattering (CEvNS) is a process in which a neutrino interacts with a nucleus, giving it a recoil kick. Although the probability for such a process to occur is relatively high, the process has never before been detected because typical nuclear recoil energies are very small. Because the rate of the process can be quite precisely predicted, a deviation of measurement from prediction could indicate new physics beyond the Standard Model. The COHERENT experiment has made the first measurements of this process at the Spallation Neutron Source at Oak Ridge National Laboratory in Tennessee, and is currently pursuing further measurements. This project may include design, simulation, background evaluation, and data analysis work. The student will gain experience with a variety of simulation and data analysis software tools. Programming experience will be useful but is not required.

6. Development of Statistical Projections for Measurements at the EIC
Advisor:  Anselm Vossen 
Student:  Megan Sturm

The Electron Ion Collider (EIC) will be built over the next decade at Brookhaven National Laboratory.  Using deep-inelastic scattering reactions, this machine allows the study of the quark-gluon structure of the nucleon with unprecedented precision. Currently, the community is developing a Yellow Report to refine the parameters of the detector and collider. This project aims to support this effort by implementing a weighting scheme for di-hadron observables based on theoretical projections within the established software framework of the EIC project. The results will be used to arrive at statistical projections for this observable.

This project requires proficiency in programming, in particular python and/or C++.

High Energy Physics / CERN Projects

1.  Exploring Track Trigger Parameters for SUEPs
Advisor: Kate Pachal
Student: Jessica Nelson

CERN’s ATLAS and CMS experiments were designed with prompt and standard model particles in mind. New desired searches, primarily for long lived particles (LLPs) and exotic signatures, demand new considerations in the design and implementation of hardware level track triggering algorithms.  This study will simulate such events so as to find the collection of triggering parameters that best suit a wide range of LLP and exotic signatures. One such signature is SUEPs, or stable unclustered energy patterns. We have found that for this signature, low parent masses and high transverse momentum thresholds lower efficiency substantially, while the number of tracks per event threshold is robust.  Additional efficiency testing must be conducted for displaced leptons, displaced vertices, and stable charged particles before appropriate parameters can be identified.

2. Using Machine Learning to Improve the Sensitivity of t-tbar Resonance Searches
Advisor: Mark Kruse
Student: Angelina Partenheimer

Proton-proton collisions resulting in four top quarks may be relevant for probing Beyond-Standard-Model physics, and are being studied by the ATLAS experiment on the Large Hadron Collider. While four-tops final states are predicted by the Standard Model, they are rare, and therefore difficult to detect. Even though the ATLAS experiment has evidence of the four-tops final state, it still has not achieved the 5σ certainty required to claim observation. Furthermore, in order to distinguish production of a Beyond-Standard-Model t-tbar heavy resonance the current analysis would require better resolution of the four-tops final state. This project examines the feasibility of using machine learning with TensorFlow to improve sensitivity in t-tbar resonance searches.