자료유형  

 학위논문

Control Number  

0017161019

International Standard Book Number  

9798382309903

Dewey Decimal Classification Number  

523

Main Entry-Personal Name  

Wheeler, Tyler Markham.

Publication, Distribution, etc. (Imprint  

[S.l.] : Michigan State University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

161 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

General Note  

Advisor: Wrede, Christopher;Ravishankar, Saiprasad.

Dissertation Note  

Thesis (Ph.D.)--Michigan State University, 2024.

Summary, Etc.  

요약A neutron star can accrete hydrogen-rich material from a low-mass binary companion star. This can lead to periodic thermonuclear runaways, which manifest as Type I X-ray bursts detected by space-based telescopes. Sensitivity studies have shown that 15O(α, γ) 19Ne carries one of the most important reaction rate uncertainties affecting the modeling of the resulting light curve. This reaction is expected to be dominated by a narrow resonance corresponding to the 4.03 MeV excited state in 19Ne. This state has a well-known lifetime, so only a finite value for the small alpha-particle branching ratio is needed to determine the reaction rate. Previous measurements have shown that this state is populated in the decay of 20Mg. 20Mg(βpα) 15O events through the key 15O(α, γ) 19Ne resonance yield a characteristic signature: the near simultaneous emission of a proton and alpha particle.To identify these events of interest the GADGET II TPC was used at the Facility for Rare Isotope Beams during Experiment 21072. An 36Ar primary beam was impinged on a 12C target to create a fast beam of 20Mg whose decay fed the 19Ne state of interest. The details of the development, and testing of the GADGET II system will be discussed along with the preliminary results from this experiment, which include discussion of the data processing and analysis methods being used on the newly acquired data.Moreover, convolutional neural networks (CNNs) are explored for rare event identification in the TPC data. To leverage the computational advantages of 2D CNNs and the availability of pre-trained models, early data fusion techniques have been adopted to efficiently convert the data into 2D formats. Addressing real training data scarcity and simulation discrepancies, parameter variations are incorporated in simulations to enhance model robustness, making the CNNs ultra-sensitive to subtle event indicators. The resulting ensembles deployed on the experimental data are able to identify 98% of all two-particle-events in the dataset. The techniques of this ongoing study are detailed, highlighting the promising future applications of this methodology.

Subject Added Entry-Topical Term  

Astrophysics.

Subject Added Entry-Topical Term  

Computational physics.

Subject Added Entry-Topical Term  

Nuclear physics.

Index Term-Uncontrolled  

Neutron stars

Index Term-Uncontrolled  

Convolutional neural networks

Index Term-Uncontrolled  

X-ray bursts

Added Entry-Corporate Name  

Michigan State University Physics - Doctor of Philosophy

Host Item Entry  

Dissertations Abstracts International. 85-10B.

Electronic Location and Access  

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Control Number  

joongbu:653967