arXiv subjects

T. Stuck

Publications and source records attributed to T. Stuck.

3 recordsLinked to original sources

arXiv2026

Excited states of $^{148}$Nd studied via the $^{150}$Nd$(p,t){}^{148}$Nd reaction and the observation of possible low-spin two-phonon octupole states at $N=88$

We report new data from a $^{150}$Nd$(p,t){}^{148}$Nd experiment performed at the John D. Fox Accelerator Laboratory of Florida State University. In total, 54 excited states of $^{148}$Nd were observed up to an excitation energy of 3500 keV. In this work, we focus on $0^+$ states and their band members. In contrast to previous work, the $0^+_3$ band is proposed as the candidate for the two-phonon octupole vibrational band. Supporting $spdf$ IBM-1 calculations are presented. To test the robustness of the IBM calculations, several observables were interrogated and are discussed in this publication. In addition, we make the case that neither the $0^+_2$ nor the $0^+_3$ states of the other $N=88$ isotones are likely good candidates for two-phonon octupole states. Based on our new data for $^{148}$Nd, we propose candidates in $^{150}$Sm and $^{152}$Gd. Using available $\gamma$-decay data for states with moderate spins in the yrast sequence and a comparison to IBM calculations, we also show how the staggering of the $B(E1)/B(E2)$ ratios in the yrast sequence can possibly be used to probe the appearance of bands with multiple octupole phonons.

nucl-ex↗

arXiv2026

The ICESPICE demonstrator for particle/$\gamma$-$e^{-}$ coincidence experiments at Florida State University

The Internal Conversion Electron SPectrometer In Coincidence Experiments (ICESPICE) demonstrator has been developed at Florida State University to enable particle/gamma-electron coincidence measurements in low-energy nuclear structure studies. ICESPICE is based on the mini-orange spectrometer concept and features a modular design using commercially available permanent magnets arranged in toroidal configurations to transport internal conversion electrons to room-temperature PIPS detectors while suppressing background from undesired particles. The system was optimized through SolidWorks modeling, COMSOL magnetic field simulations, and Geant4 particle tracking to maximize the magnetic transmission probability for electrons around 1 MeV. Commissioning tests using a calibrated 207Bi source demonstrated the performance of multiple spectrometer-detector configurations. Coincidence measurements between CeBr3 detectors from the CeBrA array and PIPS detectors revealed clear gamma-electron correlations. The first in-beam particle-electron measurements using ICESPICE were performed with the Super-Enge Split-Pole Spectrograph (SE-SPS) in the 208Pb(d,t)207Pb reaction. Prompt coincidences between tritons detected with the SE-SPS and electrons detected with ICESPICE were observed. The presented results show that ICESPICE is a promising ancillary detector system for in-beam internal conversion electron spectroscopy at the FSU SE-SPS.

physics.ins-det↗

arXiv2024

Numerical Simulations for Fractional Differential Equations of Higher Order and a Wright-Type Transformation

In this work, a new relationship is established between the solutions of higher fractional differential equations and a Wright-type transformation. Solutions could be interpreted as expected values of functions in a random time process. As applications, we solve the fractional beam equation, fractional electric circuits with special functions as external sources, and derive dAlemberts formula for the fractional wave equation. Due to this relationship, we present two methods for simulating solutions of fractional differential equations. The two approaches use the interpretation of the Caputo derivative of a function as a Wright-type transformation of the higher derivative of the function. In the first approach, we use the Runge-Kutta method of hybrid orders 4 and 5 to solve ordinary differential equations combined with the Monte Carlo integration to conduct the Wrighttype transformation. The second method uses a feedforward neural network to simulate the fractional differential equation.

math.NA↗