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Emulators and their applications in low-energy nuclear physics

Garcia, Alberto Jose
http://orcid.org/0000-0003-1723-3225
http://rave.ohiolink.edu/etdc/view?acc_num=osu169082656702898

Abstract Details

2023, Doctor of Philosophy, Ohio State University, Physics.
Nuclear physics is the study of phenomena involving the strong interaction, through its fundamental theory of quantum chromodynamics (QCD) or through effective theories with composite degrees of freedom. It seeks to understand the origin and evolution of visible matter; the organizational principles and emergent phenomena of nuclei; how the fundamental interactions can be understood using the nucleus as a laboratory; and how nuclear physics research can benefit society. To make progress, experimental facilities, such as the Facility of Rare Isotope Beams (FRIB) and ATLAS, are continuously collecting data to further our understanding of atomic nuclei and astrophysical processes. At the same time, theoretical approaches are tasked with providing accurate models whose theoretical errors are well-known. By working together, theoretical results can help guide experimentalists in understanding which regions and measurements provide the most return. The results presented in this thesis contribute to this effort by describing the construction of surrogate models (i.e., emulators) that produce fast, but accurate predictions for bound and scattering systems with various interactions. Emulators provide the means necessary for obtaining well-defined errors of a theoretical model by accurately reproduce their high-fidelity counterpart in a fraction of the time it takes to solve the underlying physics equations. These emulators can make feasible the use of Bayesian parameter estimation in low-energy nuclear physics by using the emulators to make predictions with many different parameter sets, allowing one to properly and efficiently propagate the theoretical uncertainties to NN observables. When used in conjunction with Bayesian experimental design, it allows one to establish the optimal experimental design needed to produce quality measurements in a much faster way compared to traditional calculations. The emulators described in this thesis fall into two categories: model-driven and data-driven. The data-driven approach relies only on the output of high-fidelity calculations in order to train the model, thus acting like a black box problem in regards to the physics. These are machine learning approaches such as neural networks and Gaussian processes. On the other hand, the model-driven approach relies on deriving a set of reduced-order equations that incorporate all the important underlying physics needed to make accurate predictions by projecting the high-dimensional space into a low-dimension manifold. The process of obtaining the reduced-order equations can be further described through the use of variational and Galerkin projection methods, which allow us a way of producing an efficient basis. This thesis demonstrates these points by describing the construction of different emulators from variational and Galerkin projection methods, emphasizing the benefits of offline-online decompositions, and showing how these concepts lead to fast & accurate emulators for bound and scattering systems. We then apply these emulators to various interactions with many different model parameter sets and compare the results with their respective high-fidelity model and each other in order to determine the efficiency of the different emulators in making predictions. We also explore the use of neural networks and their effectiveness in extrapolating the ground-state observables of different nuclei, and use them to examine the infrared (IR) and ultraviolet (UV) dependence of the no-core shell model (NCSM) model space to detect correlations between observables of different nuclei. Furthermore, we point to future extensions and applications of these emulators in nuclear physics.
Dick Furnstahl (Advisor)
Yuri Kovchegov (Committee Member)
Richard Hughes (Committee Member)
Douglass Schumacher (Committee Member)
182 p.
Physics
emulators; reduced-order models; nuclear physics; scattering physics; neural networks; no-core shell model

Recommended Citations

Citations

  • Garcia, A. J. (2023). Emulators and their applications in low-energy nuclear physics [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu169082656702898

    APA Style (7th edition)

  • Garcia, Alberto. Emulators and their applications in low-energy nuclear physics. 2023. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu169082656702898.

    MLA Style (8th edition)

  • Garcia, Alberto. "Emulators and their applications in low-energy nuclear physics." Doctoral dissertation, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu169082656702898

    Chicago Manual of Style (17th edition)

osu169082656702898
38
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This open access ETD is published by The Ohio State University and OhioLINK.