Accurate prediction of gamow-teller beta-decay matrix elements via machine learning: Implications for nuclear structure
| dc.authorid | 0000-0002-7508-7548 | |
| dc.contributor.author | Yeşilkanat, Cafer Mert | |
| dc.contributor.author | Akkoyun, Serkan | |
| dc.date.accessioned | 2026-07-16T05:32:47Z | |
| dc.date.available | 2026-07-16T05:32:47Z | |
| dc.date.issued | 2025 | |
| dc.department | AÇÜ, Eğitim Fakültesi, Matematik ve Fen Bilimleri Eğitimi Bölümü | |
| dc.description.abstract | Accurate prediction of Gamow-Teller (GT) beta decay matrix elements [M(GT)] is essential for elucidating complex nuclear structure phenomena and understanding astrophysical processes. In this study, we employed five advanced machine learning models (Cubist, Support Vector Regression, Extreme Gradient Boosting, Random Forest, and Bayesian Regularized Neural Networks) to predict GT beta decay matrix elements in sd-shell nuclei, using experimental data from NNDC/ENSDF, NUBASE2016, and AME2016. This study systematically compared the predictive performance of traditional theoretical approaches (including the USDB, IM-SRG, CCEI, and CEFT) to that of advanced machine learning models trained based on experimental observations. Our primary objective was to determine whether data-driven models could achieve higher predictive accuracy than computationally expensive theoretical models by learning the complex and nonlinear relationships among experimental parameters that reflect nuclear structure and decay dynamics. The results demonstrate that the Cubist model achieves a significantly lower RMSE (0.073 in the full parameter modeling approach and 0.112 in the reduced parameter modeling approach) and high coefficients of determination (R2 = 0.901 and 0.919, respectively), thereby outperforming traditional methods. Furthermore, SHapley Additive exPlanations (SHAP) analysis revealed that a minimal set of critical nuclear parameters predominantly governs GT decay dynamics, thereby enhancing model interpretability without compromising predictive accuracy. Complementing these findings, an online calculator was developed to facilitate rapid, highfidelity predictions of GT matrix elements. Overall, our study demonstrates that a data-driven approach outperforms established theoretical models. More importantly, by identifying the minimal set of physical observables that govern GT transitions, our work provides crucial insights into the underlying physics of nuclear structure and offers a new benchmark for refining future theoretical models and astrophysical calculations. | |
| dc.identifier.doi | 10.1016/j.nuclphysa.2025.123318 | |
| dc.identifier.issn | 0375-9474 | |
| dc.identifier.issn | 1873-1554 | |
| dc.identifier.uri | https://hdl.handle.net/11494/6337 | |
| dc.identifier.volume | 1067 | |
| dc.identifier.wos | WOS:001654703800001 | |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.institutionauthor | Yeşilkanat, Cafer Mert | |
| dc.institutionauthorid | 0000-0002-7508-7548 | |
| dc.language.iso | en | |
| dc.publisher | ELSEVIER | |
| dc.relation.ispartof | NUCLEAR PHYSICS A | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Nuclear structure | |
| dc.subject | Gamow-teller | |
| dc.subject | Beta decay | |
| dc.subject | Machine learning | |
| dc.subject | SHAP | |
| dc.title | Accurate prediction of gamow-teller beta-decay matrix elements via machine learning: Implications for nuclear structure | |
| dc.type | Article |
