Araştırma Makalesi
Yıl 2024,
Cilt: 45 Sayı: 1, 160 - 163, 28.03.2024
Öz
Experimental and theoretical studies on halo nuclei, whose nucleon binding energies are extremely weak, are among the most interesting topics of nuclear physics studies. By better defining and understanding this unusual behavior of these nuclei, our understanding of nuclear structure can be further improved. Although there are already a few experimentally proven halo nuclei in the literature, many others have found their place in the literature as candidate halo nuclei. In this study, the classification of halo nuclei was carried out using an artificial neural network approach. In the light nuclei region, the properties of nuclei, including halo nuclei, were discussed and the existing halo nuclei were classified. The success of the obtained results indicates that machine learning methods can be used for identifying halo nuclei. Thus, these methods are considered as one of the alternative tools to confirm the existence of new or candidate halo nuclei.
Anahtar Kelimeler
Nuclear structure halo nucleus machine-learning classification
Kaynakça
- [1] Al-Khalili J., An Introduction to Halo Nuclei, Lect. Notes Phys. 651 (2004) 77–112.
- [2] Tanihata I., Hamagaki H., Hashimoto O., Shida Y., Yoshikawa N., Sugimoto K., Yamakawa O., Kobayashi T., Takahashi N., Measurements of Interaction Cross Sections and Nuclear Radii in the Light p-Shell Region, Phys. Rev. Lett. 55 (1985) 2676.
- [3] Hansen P.G., Jonson B., The Neutron Halo of Extremely Neutron-Rich Nuclei, Europhys. News 4 (1987) 409–414.
- [4] Prathapan K., Deneshan P., Damodaran L., Biju R.K., Theoretical study on neutron halo emission from heavy elements, Nuclear and Particle Physics Proceedings 336–338 (2023) 45-47.
- [5] Tanihata I., Savajols H., Kanungo R., Recent experimental progress in nuclear halo structure studies, Progress in Particle and Nuclear Physics 68 (2013) 215-313.
- [6] Zhukov M.V., Danilin B.V., Fedorov D.V., Bang J.M., Thompson I.J., Vaagen J.S., Bound state properties of Borromean halo nuclei: 6He and 11Li, Phys. Rep. 231 (1993) 151–199.
- [7] Pieper S.C., Wiringa R.B., Quantum Monte Carlo Calculations of Light Nuclei, Annu. Rev. Nucl. Part. Sci. 51 (2001) 53–90.
- [8] Dean D.J., Jensen M.H., Pairing in nuclear systems: from neutron stars to finite nuclei, Red. Mod. Phys., 75 (2003) 607–656.
- [9] Sharma Mahesh K., Panda R. N., Sharma Manoj K., Patra S. K., Search for halo structure in 37Mg using the Glauber model and microscopic relativistic mean-field densities, Phys. Rev. C, 93 (2016) 014322.
- [10] Kamimura M., Yahiro M., Iseri Y., Sakuragi Y., Kameyama H., Kawai M., Projectile Breakup Processes in Nuclear Reactions, Prog. Theor. Phys. Suppl. 89 (1986) 1–10.
- [11] Ono A., Horiuchi H., Maruyama T., Ohnishi A., Fragment formation studied with antisymmetrized version of molecular dynamics with two-nucleon collisions, Phys. Rev. Lett., 68 (1992) 2898–2900.
- [12] Varga V., Suzuki Y., Lovas R.G., Microscopic multicluster description of neutron-halo nuclei with a stochastic variational method, Nucl. Phys. A 571 (1994) 447–466.
- [13] Ryberg E., Forssen C., Hammer H.W., Platter L., Range corrections in Proton Halo Nuclei, arXiv:1507.08675v1 [nucl-th] (2015).
- [14] Haykin S., “Neural Networks: a Comprehensive Foundation” Englewood Cliffs, Prentice-Hall, New Jersey, pp.842, 1999.
- [15] Bayram T., Akkoyun S., Şentürk Ş., Adjustment of Non-linear Interaction Parameters for Relativistic Mean Field Approach by Using Artificial Neural Networks, Physics of Atomic Nuclei, 81 (2018) 288-295.
- [16] Akkoyun S., Time-of-flight discrimination between gamma-rays and neutrons by neural networks, Annals of Nuclear Energy, 55 (2013) 297-301.
- [17] Akkoyun S., Bayram T., Kara S.O., Yildiz N., Consistent empirical physical formulas for potential energy curves of 38–66Ti isotopes by using neural networks, Physics of Particles and Nuclei Letters, 10 (2013) 528-534.
- [18] Akkoyun S., Bayram T., Kara S.O., Sinan A., An artificial neural network application on nuclear charge radii, Journal of Physics G: Nuclear and Particle Physics, 40 (2013) 055106.
- [19] Bayram T., Akkoyun S., Kara S.O., A study on ground-state energies of nuclei by using neural networks, Annals of Nuclear Energy, 63 (2014) 172-175.
- [20]Akkoyun S., Estimation of fusion reaction cross-sections by artificial neural networks, Nuclear Instruments and Methods in Physics Research Section B, 462 (2020) 51-54.
Yıl 2024,
Cilt: 45 Sayı: 1, 160 - 163, 28.03.2024
Öz
Kaynakça
- [1] Al-Khalili J., An Introduction to Halo Nuclei, Lect. Notes Phys. 651 (2004) 77–112.
- [2] Tanihata I., Hamagaki H., Hashimoto O., Shida Y., Yoshikawa N., Sugimoto K., Yamakawa O., Kobayashi T., Takahashi N., Measurements of Interaction Cross Sections and Nuclear Radii in the Light p-Shell Region, Phys. Rev. Lett. 55 (1985) 2676.
- [3] Hansen P.G., Jonson B., The Neutron Halo of Extremely Neutron-Rich Nuclei, Europhys. News 4 (1987) 409–414.
- [4] Prathapan K., Deneshan P., Damodaran L., Biju R.K., Theoretical study on neutron halo emission from heavy elements, Nuclear and Particle Physics Proceedings 336–338 (2023) 45-47.
- [5] Tanihata I., Savajols H., Kanungo R., Recent experimental progress in nuclear halo structure studies, Progress in Particle and Nuclear Physics 68 (2013) 215-313.
- [6] Zhukov M.V., Danilin B.V., Fedorov D.V., Bang J.M., Thompson I.J., Vaagen J.S., Bound state properties of Borromean halo nuclei: 6He and 11Li, Phys. Rep. 231 (1993) 151–199.
- [7] Pieper S.C., Wiringa R.B., Quantum Monte Carlo Calculations of Light Nuclei, Annu. Rev. Nucl. Part. Sci. 51 (2001) 53–90.
- [8] Dean D.J., Jensen M.H., Pairing in nuclear systems: from neutron stars to finite nuclei, Red. Mod. Phys., 75 (2003) 607–656.
- [9] Sharma Mahesh K., Panda R. N., Sharma Manoj K., Patra S. K., Search for halo structure in 37Mg using the Glauber model and microscopic relativistic mean-field densities, Phys. Rev. C, 93 (2016) 014322.
- [10] Kamimura M., Yahiro M., Iseri Y., Sakuragi Y., Kameyama H., Kawai M., Projectile Breakup Processes in Nuclear Reactions, Prog. Theor. Phys. Suppl. 89 (1986) 1–10.
- [11] Ono A., Horiuchi H., Maruyama T., Ohnishi A., Fragment formation studied with antisymmetrized version of molecular dynamics with two-nucleon collisions, Phys. Rev. Lett., 68 (1992) 2898–2900.
- [12] Varga V., Suzuki Y., Lovas R.G., Microscopic multicluster description of neutron-halo nuclei with a stochastic variational method, Nucl. Phys. A 571 (1994) 447–466.
- [13] Ryberg E., Forssen C., Hammer H.W., Platter L., Range corrections in Proton Halo Nuclei, arXiv:1507.08675v1 [nucl-th] (2015).
- [14] Haykin S., “Neural Networks: a Comprehensive Foundation” Englewood Cliffs, Prentice-Hall, New Jersey, pp.842, 1999.
- [15] Bayram T., Akkoyun S., Şentürk Ş., Adjustment of Non-linear Interaction Parameters for Relativistic Mean Field Approach by Using Artificial Neural Networks, Physics of Atomic Nuclei, 81 (2018) 288-295.
- [16] Akkoyun S., Time-of-flight discrimination between gamma-rays and neutrons by neural networks, Annals of Nuclear Energy, 55 (2013) 297-301.
- [17] Akkoyun S., Bayram T., Kara S.O., Yildiz N., Consistent empirical physical formulas for potential energy curves of 38–66Ti isotopes by using neural networks, Physics of Particles and Nuclei Letters, 10 (2013) 528-534.
- [18] Akkoyun S., Bayram T., Kara S.O., Sinan A., An artificial neural network application on nuclear charge radii, Journal of Physics G: Nuclear and Particle Physics, 40 (2013) 055106.
- [19] Bayram T., Akkoyun S., Kara S.O., A study on ground-state energies of nuclei by using neural networks, Annals of Nuclear Energy, 63 (2014) 172-175.
- [20]Akkoyun S., Estimation of fusion reaction cross-sections by artificial neural networks, Nuclear Instruments and Methods in Physics Research Section B, 462 (2020) 51-54.
Toplam 20 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Nükleer Fizik |
| Bölüm | Natural Sciences |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Mart 2024 |
| Gönderilme Tarihi | 9 Ocak 2024 |
| Kabul Tarihi | 27 Şubat 2024 |
| Yayımlandığı Sayı | Yıl 2024Cilt: 45 Sayı: 1 |
