S. R. Carmichael | Searcharxiv

arXiv2026

Observation of a dominant $\boldsymbol{0f_{7/2}}$ neutron configuration in the $\boldsymbol{^{32}}$Si $\boldsymbol{J^π=5^-}$ isomeric state

An yrast, $J^π=5^-$, spin-trap isomer has been previously identified in $^{32}$Si. The isomeric state decays predominantly via a hindered $E3$ transition [B($E3$) = 0.0841(10)~W.u.], bypassing a nearby $E2$ decay path to the first excited $3^-$ level. The single-neutron aspects of these negative parity levels were investigated via the $^{31}$Si$(d$,$p)^{32}$Si reaction at 9.6~MeV/$u$ using HELIOS and the ATLAS in-flight facility. The $5^-$ state appears as a dominant $\ell=3$ transfer with a relatively large spectroscopic factor, confirming its single-particle $\nu0f_{7/2}$ character. The yrast $3^-$ level had a reduced $\ell=3$ spectroscopic factor of $\approx$ 0.44 compared to that of the $5^-_1$ level. This is similar to the situation observed in nearby $^{34}$S which by contrast has a measured B($E2, 5^-\rightarrow 3^-$) transition strength closer to 1~W.u.. It has been concluded that the hinderance of the $5^-_1\rightarrow 3^-_1$ transition in $^{32}$Si is not primarily due to the differing overlaps in the neutron structure. Instead, the lack of participation by both the protons and the neutrons in the transition is proposed as the transition-strength reduction mechanism.

nucl-ex↗

arXiv2025

Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

physics.ins-det↗