Bulletin of the American Physical Society
6th Joint Meeting of the APS Division of Nuclear Physics and the Physical Society of Japan
Sunday–Friday, November 26–December 1 2023; Hawaii, the Big Island
Session M06: Undergraduate Research III |
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Chair: Mike Youngs, Texas A&M Room: Hilton Waikoloa Village Queens 5 |
Friday, December 1, 2023 2:00PM - 2:15PM |
M06.00001: Towards Rydberg State Ionization of Aluminum Alvin Grullon, Alejandro Ortiz-Cortes The possible abnormal proton-halo structure in 22Al will be studied in the BECOLA facility through laser spectroscopy. As a preparatory study, this contribution has summarized the efforts towards the development of a two steps resonance ionization scheme using Rydberg states for stable 27Al. The development of the scheme has been pursued in two different directions. On one side, the Sirah Cobra dye laser has been prepared to study the Rydberg states on the 420-460 nm wavelength range. On the other side, the hyperfine spectrum of the 3s23p2P3/2 → 3s24s2S1/2 transition has been measured using CLS fluorescence spectroscopy, extracting the hyperfine coupling constants. |
Friday, December 1, 2023 2:15PM - 2:30PM |
M06.00002: Streamlining Nuclear Physics Data and Uncertainty Quantification with the Bayesian Mass Explorer Landon Buskirk, Kyle S Godbey, Pablo G Giuliani, Witold Nazarewicz, Yukari Yamauchi Obtaining up-to-date, reliable data is a necessary challenge across all domains of science, especially in nuclear physics. Nuclear data with uncertainties are often found in different places and formats, requiring significant effort to properly consolidate and compare. To address these challenges, the Bayesian Mass Explorer (BMEX) aims to provide an open-source suite of user-friendly web applications for on-the-fly data retrieval, visualization, and Bayesian uncertainty quantification. BMEX Masses, the project’s flagship app, focuses primarily on plotting experimental and model data, allowing for immediate model performance analysis and experimental feature extraction. BMEX also serves as a cloud-enabled stage for projects leveraging machine learning and advanced statistics, such as reduced basis methods and neural networks. In a current project, neural networks are trained to learn a normalizing flow that learns Bayesian posterior distributions of model parameters for a relativistic mean field mass model. These networks can then be deployed to a web app in BMEX to quickly sample parameters and generate quantified predictions. In the future BMEX will continue to provide new user-focused, accessible tools to the nuclear physics community in the fields of model emulation, online model calibration, and experimental design. |
Friday, December 1, 2023 2:30PM - 2:45PM |
M06.00003: Uncertainty Quantification at the Edge of Stability Andrew R Yeomans-Stephenson, Kyle S Godbey, Pablo G Giuliani, Joshua Wylie The properties of exotic nuclei are a vital importance to underastand the origin of elements. However, studying the properties experimentally can pose great challenges. In this work we use cutting edge quantified theoretical models to study reactions between exotic oxygen isotopes and stable calcium targets to predict fusion cross sections in an effort to guide futre experiments at the Facility for Rare Isotope Beams. |
Friday, December 1, 2023 2:45PM - 3:00PM |
M06.00004: PointNetArrow: Learning Temporal Dynamics of Reactions in Time Projection Chambers Dmytro Kurdydyk, Mohamed Mostafa, Michelle P Kuchera, Raghuram Ramanujan, Yassid Ayyad, Daniel Bazin We investigate the efficacy of building a pre-trained model as a foundation for various machine learning tasks across different experiments at the Active-Target Time Projection Chamber (AT-TPC). In pursuit of this, we developed PointNetArrow, a deep neural network trained in a self-supervised fashion to determine the time evolution of AT-TPC events. PointNetArrow takes in 3D point cloud events, preprocessed into stacks of sequential frames. The model is trained on a surrogate task of predicting the direction of time of these events. Successful prediction of this `arrow of time' indicates the model's ability to decipher both low-level visual cues (such as shapes, volume, and charge) and high-level cues (like spatial relationships and temporal movements). The PointNetArrow model was trained using simulated data from a 22Mg +α experiment. This pretrained model was then evaluated on a track counting from simulated 16O +α data. Results from both the surrogate task and the downstream task will be presented. |
Friday, December 1, 2023 3:00PM - 3:15PM |
M06.00005: Detection of Protons Emitted from (p, 2p) Reactions with Straw-Tube Array Griffin L Rhoads-Albert, Heather L Crawford, Carlotta Porzio A challenge in studying neutron-rich nuclei via in-beam gamma-ray spectroscopy is the restriction in the thickness of the target. To solve this and perform high-resolution spectroscopy without the restriction of thickness, a thick target of liquid hydrogen coupled with proton-sensitive gas-filled straw-tube detectors is under development. Protons are emitted when the nucleons of the neutron-rich ion beam interact with the target in a (p,2p) reaction, and the straw-tube detectors surrounding the target exploit the ionization of the gas that occurs to detect emitted protons as they pass through. These detectors can be used to trace back to the vertex of the reaction in the target, allowing an effective Doppler correction while maximizing the reaction rate, overcoming the previous restrictions of target thickness. I will present progress on simulations using the CERN toolkit "Garfield++", being performed to determine the optimal configurations for these detectors, including their size, voltage, and arrangement. |
Friday, December 1, 2023 3:15PM - 3:30PM |
M06.00006: Analysis of Na(TI) Scintillator Using Spectroscopy Willie B Williams, Steven D Pain, Sudarsan Balakrishnan Neutron-capture cross sections on unstable nuclei are key in the creation of heavy elements in our universe. These elements are created through rapid neutron capture nucleosynthesis, called the r - process, believed to occur in neutron star mergers or core-collapse supernovae. The (d,p) reaction has recently been validated for determining (n,g) cross sections, using the Surrogate Reaction Method (SRM). Using arrays such as ORRUBA/GODDESS, the SRM can be applied with radioactive beams to determine (n,g) cross sections on unstable nuclei that can not be measured directly. In order to increase sensitivity for these measurements, high efficiency for gamma ray detection is desired. A large array of 2"x4"x16" NaI(Tl) detectors is being considered for this purpose. A few detectors for this array have been instrumented using the ORRUBA data acquisition system, and their performance evaluated using standard gamma ray sources. The individual Na(TI) detector response has been studied as a function of PMT voltage, gain, shaping time, and position of illumination by a collimated source. An overview of the conceptual array design, and details of the detector characterization, will be presented. |
Friday, December 1, 2023 3:30PM - 3:45PM |
M06.00007: Modeling Lithium Depletion in Spite Plateau Stars using MESA Henry R Bloss, Grant J Mathews The theory of Big Bang Nucleosynthesis (BBN) provides an explanation for the origin of light elements during the first few moments of the universe. Though this theory corresponds well with observational measurements of light elements, this agreement breaks down in the observation of 7Li in old, low-metallicity HALO stars. This disagreement, dubbed the "Lithium problem," poses a roadblock for BBN; if a solution is not found, BBN must be revisited. My research explores solutions to the lithium problem which do not force us to reconsider BBN. Instead we explore potential solutions which rely on thermonuclear destruction of lithium within a star, the most intriguing of which convective overshoot and mixing length theory (MLT), over the 1010 year lifetime of the star. To test these solutions, I employ the code Modules for Experimentation in Stellar Astrophysics (MESA), a 1D stellar evolution code. I use this code to generate models of stars matching stellar properties of Spite plateau stars from Norris et.al. [1]. We impose astrophysical schemes on these models, and explore various scenarios to induce gradual thermonuclear destruction of 7Li bringing its abundance closer to the value predicted by BBN. In particular, we show that the observed lithium resides in a thin surface convective layer for which gradual mixing into the interior is possible. |
Friday, December 1, 2023 3:45PM - 4:00PM |
M06.00008: Characterization of the SuperHeavy RECoils (SHREC) detector at Berkeley Gas-filled Separator (BGS) Patrick R Francisco, Rodney Orford, Fatima H. Garcia, Jacklyn M Gates At the 88-inch cyclotron located in Lawrence Berkeley National Laboratory, there have been great efforts to study the physical and chemical properties of heavy and superheavy elements (SHE). Utilizing the Berkeley Gas-filled Separator (BGS), rare isotope beams produced in fusion-evaporation reactions are slowed and they can be analyzed using the newly implemented SuperHeavy RECoils (SHREC) detector. This new detector improves efficiency and accuracy because it contains 14 double sided silicon detectors and offers a solid angle coverage of 80%. The SHREC detector is specifically designed for studying the radioactive decay properties of superheavy atoms. SHREC is used for detecting alpha particles, which are particles that emit from unstable nuclei with characteristic energies. The SHREC energy response is calibrated with sealed alpha sources. When an alpha particle implants into the detector, it will lose energy as it travels through the detector dead layer before depositing its energy into the active region. In order to quantify the dead layer, I compared experimental energy loss data to simulated detector response using the Monte Carlo software, SRIM. I will explore updates on the energy characterization of SHREC and discuss my results in the context of recently studied alpha decay chains of element-114. |
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