arXiv subjects

Yukiya Saito

Publications and source records attributed to Yukiya Saito.

6 recordsLinked to original sources

arXiv2026

Future directions in nuclear $β$ decay at FRIB and beyond

Motivated by the opportunities presented for studies relevant to nuclear structure, astrophysics, and fundamental symmetries with nuclear $β$ decay, the Facility for Rare Isotope Beams (FRIB) Theory Alliance topical program ``Future Directions in Nuclear $β$ Decays at FRIB'' was held in September of 2025. This white paper summarizes the main points of discussion over the two-week program, and it aims to provide a snapshot of the current status of the field while also highlighting important questions and opportunities for future work. We provide an overview of the experimental tools and techniques that enable modern $β$ decay studies, discuss the current state of nuclear many-body approaches used to study $β$ decays, and highlight the important science questions that can be addressed by weak decays.

nucl-th↗

arXiv2025

Effect of Finite-Temperature $β$-Decay Rates on the Rapid Neutron Capture Process

$β$-decay is known to play an essential role in the rapid neutron capture process ($r$-process) during $(n, γ) \leftrightarrow (γ, n)$ equilibrium and freeze-out when the neutron-rich nuclei decay back to stability. Recent systematic theoretical studies on $β$-decay at finite temperature indicated that under hot conditions ($T\sim10$~GK), a significant acceleration of $β$-decay rates is expected, especially for nuclei near stability. This corresponds to the early stage of the $r$-process. In this study, we investigate the effect of the $β$-decays in finite temperature using the rates calculated with the finite-temperature proton-neutron relativistic quasiparticle random-phase approximation (FT-PNRQRPA). We explore a variety of astrophysical conditions and find that the effect on the abundance pattern is significant in hot and moderately neutron-rich conditions such as are expected in magnetorotational supernovae. Accelerated $β$-decay rates also increase the heating rate in the early phase, resulting in an additional modification of the final abundance pattern.

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arXiv2025

Antimatter Annihilation Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber

The ALPHA-g experiment at CERN aims to precisely measure the terrestrial gravitational acceleration of antihydrogen atoms. A radial Time Projection Chamber (rTPC), that surrounds the ALPHA-g magnetic trap, is employed to determine the annihilation location, called the vertex. The standard approach requires identifying the trajectories of the ionizing particles in the rTPC from the location of their interaction in the gas (spacepoints), and inferring the vertex positions by finding the point where those trajectories (helices) pass closest to one another. In this work, we present a novel approach to vertex reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR), directly learns the relation between the location of the vertices and the rTPC spacepoints, thus eliminating the need to identify and fit the particle tracks. PEAR shows strong performance in reconstructing vertical vertex positions from simulated data, that is superior to the standard approach for all metrics considered. Furthermore, the deep learning approach can reconstruct the vertical vertex position when the standard approach fails.

physics.ins-det↗

arXiv2024

Emulation of the final r-process abundance pattern with a neural network

This work explores the construction of a fast emulator for the calculation of the final pattern of nucleosynthesis in the rapid neutron capture process (the $r$-process). An emulator is built using a feed-forward artificial neural network (ANN). We train the ANN with nuclear data and relative abundance patterns. We take as input the $β$-decay half-lives and the one-neutron separation energy of the nuclei in the rare-earth region. The output is the final isotopic abundance pattern. In this work, we focus on the nuclear data and abundance patterns in the rare-earth region to reduce the dimension of the input and output space. We show that the ANN can capture the effect of the changes in the nuclear physics inputs on the final $r$-process abundance pattern in the adopted astrophysical conditions. We employ the deep ensemble method to quantify the prediction uncertainty of the neutal network emulator. The emulator achieves a speed-up by a factor of about 20,000 in obtaining a final abundance pattern in the rare-earth region. The emulator may be utilized in statistical analyses such as uncertainty quantification, inverse problems, and sensitivity analysis.

nucl-th↗

arXiv2024

AI Meets Antimatter: Unveiling Antihydrogen Annihilations

The ALPHA-g experiment at CERN aims to perform the first-ever direct measurement of the effect of gravity on antimatter, determining its weight to within 1% precision. This measurement requires an accurate prediction of the vertical position of annihilations within the detector. In this work, we present a novel approach to annihilation position reconstruction using an ensemble of models based on the PointNet deep learning architecture. The newly developed model, PointNet Ensemble for Annihilation Reconstruction (PEAR) outperforms the standard approach to annihilation position reconstruction, providing more than twice the resolution while maintaining a similarly low bias. This work may also offer insights for similar efforts applying deep learning to experiments that require high resolution and low bias.

physics.data-an↗

arXiv2024

Uncertainty Quantification of Mass Models using Ensemble Bayesian Model Averaging

Developments in the description of the masses of atomic nuclei have led to various nuclear mass models that provide predictions for masses across the whole chart of nuclides. These mass models play an important role in understanding the synthesis of heavy elements in the rapid neutron capture ($r$-) process. However, it is still a challenging task to estimate the size of uncertainty associated with the predictions of each mass model. In this work, a method to quantify the mass uncertainty using \textit{ensemble Bayesian model averaging} (EBMA) is introduced. This Bayesian method provides a natural way to perform model averaging, selection, calibration, and uncertainty quantification, by combining the mass models as a mixture of normal distributions, whose parameters are optimized against the experimental data, employing the Markov chain Monte Carlo (MCMC) method using the No-U-Turn sampler (NUTS). The average size of our best uncertainty estimates of neutron separation energies based on the AME2003 data is 0.48 MeV and covers 95% of new data in the AME2020. The uncertainty estimates can also be used to detect outliers with respect to the trend of experimental data and theoretical predictions.

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