D. Bardayan | Searcharxiv

arXiv2025

Direct measurement of $^{59}$Cu($p$,$\alpha$)$^{56}$Ni precludes a strong NiCu cycle in Type-I X-ray bursts

Model-observation comparisons of type-I X-ray bursts (XRBs) can reveal the properties of accreting neutron star systems, including the neutron star compactness. XRBs are powered by nuclear burning and a handful of reactions have been shown to impact the model results. Reactions in the NiCu cycles, featuring a competition between $^{59}$Cu($p$,$\gamma$)$^{60}$Zn and $^{59}$Cu($p$,$\alpha$)$^{56}$Ni, have been shown to be among the most important reactions as they are a critical checkpoint in $rp$-process flow and significantly impact the light curves and burst ashes. We report a direct measurement of $^{59}$Cu($p$,$\alpha$)$^{56}$Ni bringing stringent constraints on this reaction rate. New results rule out a strong NiCu cycle in XRBs, with a negligible degree of recycling, $\leq$5\% up to 1.5 GK. The new reaction rate, when varied within new uncertainty limits, shows no impact on one-zone XRB model light-curves tailored for clocked-burster $\tt{GS 1826-24}$, hence removing an important nuclear physics uncertainty in the model-observation comparison.

astro-ph.HE↗

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↗