arXiv subjects

J. S. Nielsen

Publications and source records attributed to J. S. Nielsen.

3 recordsLinked to original sources

arXiv2026

Precision $β$-delayed charged-particle emission spectroscopy at FRIB: Proof of principle with the $β$-decay of $^{25}\mathrm{Si}$

We report on the $β$-delayed proton and $γ$-ray emission from $^{25}\mathrm{Si}$, measured at the Facility for Rare Isotope Beams (FRIB). Low-energy $^{25}\mathrm{Si}$ ions extracted from the Advanced Cryogenic Gas Stopper were implanted into a thin carbon foil surrounded by a compact, highly segmented array of silicon detector telescopes and two high-purity germanium detectors. This setup provides high-resolution charged-particle spectroscopy, establishing a proof of principle for precision stopped-beam decay studies at FRIB. We reconstruct the $^{25}\mathrm{Si}$ decay scheme, resolving new high-energy proton transitions and determining the feeding to excited states in $^{24}\mathrm{Mg}$. The observation of spectral interference patterns enables firm spin and parity assignments for highly excited states in $^{25}\mathrm{Al}$. The $^{25}\mathrm{Si}$ $β$-strength distribution is extracted and compared with large-scale shell-model calculations.

nucl-ex↗

arXiv2026

A ground state $^{22}$Al halo is unlikely

We report the decisive resolution of the ground state spin and parity of the proton-dripline nucleus $^{22}$Al, a prime candidate for a proton halo. The resolution stems from the first $β$-delayed charged particle emission experiment in the Gas Stopping Area at the Facility for Rare Isotope Beams (FRIB), leveraging high-intensity, low-energy beams extracted from the Advanced Cryogenic Gas Stopper (ACGS). The pristine beam quality from FRIB and the ACGS enabled a sensitive particle identification technique using thin silicon detectors, allowing for the suppression of the dominant proton background and the first observation of the weak $β$-delayed $α$ transition from the Isobaric Analog State in $^{22}$Mg to the $^{18}$Ne ground state. This observation uniquely fixes the $^{22}$Al ground state as $4^+$. The valence proton is confined by a dominant $d$-wave centrifugal barrier which, combined with the Coulomb repulsion, hinders the tunneling required for halo formation despite the exceptionally low proton separation energy of $^{22}$Al.

nucl-ex↗

arXiv2021

Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes

Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian networks, evaluate a set of pre-defined heuristic decision rules to reasonably simplify the decision problem. However, the resulting policies may be compromised by the limited space considered in the definition of the decision rules. Avoiding this limitation, Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations, in which the optimal actions are prescribed as a function of the entire, dynamically updated, state probability distribution. In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers for solving the underlying planning optimization problem. Within the numerical experiments, POMDP and heuristic-based policies are thoroughly compared, and results showcase that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings.

cs.AI↗