Excited-State Half-Lives in Cd 130 and the Isospin Dependence of Effective Charges
Not Available
Jungclaus, A.↗
Engineering topics
Chae, K. Y.
Publications and source records attributed to Chae, K. Y..
TexAT detector upgrade for 14 O($α$, $p$) 17 F cross section measurement
A direct cross-section measurement of the 14 O($α$, $p$) 17 F reaction is important to understand the light curves of x-ray bursts. The measurement will be performed using the Texas Active Target TPC version 2 (TexAT_v2). The TexAT_v2 aims at measuring lower energy protons from the reaction than the original TexAT. Newly developed silicon and CsI(Tl) detector arrays are added at the left, right and bottom of a modified field cage to increase its detection efficiency. Furthermore, this paper describes the overall specifications and two commissioning experiments performed at Texas A&M University.
14O(α, p)17F↗
Proton branching ratios in 22 Mg for X-ray bursts
Here, decay protons from 22 Mg energy levels populated through a previously reported 24 Mg(p, t) 22 Mg transfer reaction (Chae et al. in Phys Rev C 79:055804, 2009) have been analyzed for proton branching ratios as a follow-up analysis. The measurement was performed at the Holifield Radioactive Ion Beam Facility of Oak Ridge National Laboratory by utilizing 41-MeV proton beams and 24 Mg solid targets. Decay protons and reaction tritons were simultaneously detected with a silicon detector array. By investigating the 24 Mg(p, t) 22 Mg*(p) 21 Na channels, the proton branching ratios of five 22 Mg excited states were obtained. The measured branching ratios provide constraints on the proton partial widths of the populated 22 Mg levels, which have implications for X-ray burst nucleosynthesis.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
First Direct Measurement Constraining the Ar 34 ( α , p ) K 37 Reaction Cross Section for Mixed Hydrogen and Helium Burning in Accreting Neutron Stars
The rate of the final step in the astrophysical αp process, the 34 Ar(α,p) 37 K reaction, suffers from large uncertainties due to a lack of experimental data, despite having a considerable impact on the observable light curves of x-ray bursts and the composition of the ashes of hydrogen and helium burning on accreting neutron stars. Here, we present the first direct measurement constraining the 34Ar(α,p)37K reaction cross section, using the Jet Experiments in Nuclear Structure and Astrophysics gas jet target. The combined cross section for the 34 Ar,Cl(α,p) 37 K,Ar reaction is found to agree well with Hauser-Feshbach predictions. The 34 Ar(α,2p) 36 Ar cross section, which can be exclusively attributed to the 34 Ar beam component, also agrees to within the typical uncertainties quoted for statistical models. This indicates the applicability of the statistical model for predicting astrophysical (α,p) reaction rates in this part of the αp process, in contrast to earlier findings from indirect reaction studies indicating orders-of-magnitude discrepancies. This removes a significant uncertainty in models of hydrogen and helium burning on accreting neutron stars.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Noise signal identification in time projection chamber data using deep learning model
Deep learning has been employed in various scientific fields and has provided promising results. Here, in this study, a deep learning classifier was implemented to improve the quality of data obtained from a time projection chamber. Digital waveforms of the detected signals were classified into the following three categories: particles, noises, and particles piled up with noises. A simple 1-dimensional convolutional neural network was developed for the classification. The model demonstrated an excellent performance on the test dataset. Its practical performance was also examined using track images and particle identification plots by comparing the original and clean data without the noise signals. The comparison clearly showed that the deep learning model improved the quality of data. The current study presents an effective application of the deep learning model for the time projection chamber data.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗