Christopher M. Campbell

The observation of active ^{44}Ti in supernova remnants offers the potential to solve one of the most debated questions in modern astrophysics, the exact underlying explosion mechanism of core collapse supernovae (CCSNe). In particular, a comparison between the predicted synthesized yield of ^{44}Ti and the ejected flux is expected to allow for a determination of the mass cut of the star. Unfortunately, such comparisons are currently severely hindered by large uncertainties in the nuclear reactions governing the production and destruction of ^{44}Ti. On this note, the astrophysical ^{45}V(p,γ)^{46}Cr reaction is expected to play a decisive role. We present the first experimental information on resonant states in the ^{45}V(p,γ)^{46}Cr reaction. Excitation energies have been measured with high precision for ten previously unknown, low-spin, proton-unbound states in ^{46}Cr, and spin-parity-assignment constraints have been made using observed γ-decay patterns and shell-model calculations. We find that an ℓ=1 resonance at 714 keV dominates the ^{45}V(p,γ)^{46}Cr reaction for the entire temperature range of α-rich freezeout. Moreover, the nominal rate is found to be ∼25-50% of previous estimates for T>1.2 GK, indicating an increase in ^{44}Ti production in CCSNe of ∼20-35%.

In this work, we consider the optimization of non-invariant systems with both safety and control constraints. We present a new approach based on Bayesian optimization for the dynamic, safe and controlled optimization of such systems. Although there are other possible use cases, we focus on the application to the electron cyclotron resonance ion source VENUS. From experimental data, we have observed that VENUS behaves to first order as a non-invariant dynamic system with moving areas of instability. Our novel approach aims at providing a tool that can maintain system optimization in a safe way. This is accomplished by making sure the objective function, the beam current in the case of VENUS, does not fall under an operational minimum, while simultaneously requiring the optimization to avoid areas where VENUS is unstable. We compare the result of our approach on synthetic data modeled to mimic the behavior of VENUS with two methods from the literature, a standard Bayesian optimizer and a safe Bayesian optimizer, both adapted to deal with dynamic systems. A cross Student T-test is conducted to show the significance of the improvement given by the new method we introduce here, regarding the two preexisting methods we compared to. The results of the tests conducted on synthetic data show that the proposed method succeeds at maintaining the system optimized and obeys the predefined constraints better than the literature methods explored.