Pub Date : 2024-07-10DOI: 10.1142/s0218301324500320
T. Yarman, N. Zaim, A. Kholmetskii, Metin Arik, O. Yarman
{"title":"Systematization of β+-Decaying Atomic Nuclei: Interrelation Between Half-Life, Mass, Energy and Size","authors":"T. Yarman, N. Zaim, A. Kholmetskii, Metin Arik, O. Yarman","doi":"10.1142/s0218301324500320","DOIUrl":"https://doi.org/10.1142/s0218301324500320","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":"72 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-10DOI: 10.1142/s0218301324500332
Cong Li, Wenlong Li
{"title":"Probing the effect of background fields on coupling constants in ultraperipheral heavy ion collisions","authors":"Cong Li, Wenlong Li","doi":"10.1142/s0218301324500332","DOIUrl":"https://doi.org/10.1142/s0218301324500332","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":"85 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-05DOI: 10.1142/s0218301324500319
Ananya Babu, Lisha Damodaran, T. P. Suresh, K. Prathapan
{"title":"An alpha-decay study of superheavy nuclei within a Coulomb and Proximity Potential Model including a Q-value Dependent Surface Diffuseness Parameter","authors":"Ananya Babu, Lisha Damodaran, T. P. Suresh, K. Prathapan","doi":"10.1142/s0218301324500319","DOIUrl":"https://doi.org/10.1142/s0218301324500319","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-05DOI: 10.1142/s0218301324300091
Long-Gang Pang
The research paradigm in physics has evolved through three distinct phases: empirical observation and induction, theoretical modeling and deduction and computational numerical analysis and simulation. We are now situated within a novel epoch wherein the scientific research paradigm is increasingly shaped by the preeminence of large-scale data and artificial intelligence, particularly within the realm of AI for science applications. The advent of high-energy colliders coupled with Monte Carlo simulations has given rise to an unprecedented accumulation of data. Nested within this transformative research paradigm, machine learning and artificial intelligence technologies have been extensively harnessed for the analysis of these vast data sets. Within the domain of high-energy nuclear physics, two prevalent machine learning techniques have emerged: Bayesian analysis and deep learning. The former employs comprehensive fitting methodologies that compare extensive data sets against theoretical models, enabling the extraction of critical information pertaining to the initial nuclear structure, parton distributions, the equation of state governing hot and dense nuclear matter, and the transport coefficients of the quark–gluon plasma, among other parameters. Conversely, the latter capitalizes on the unparalleled pattern recognition capabilities of deep learning to discern robust features from high-dimensional raw data, specifically targeting individual physical parameters. This paper elucidates the fundamental principles of machine learning and delineates its potential to augment high-energy nuclear physics research endeavors.
{"title":"Studying high-energy nuclear physics with machine learning","authors":"Long-Gang Pang","doi":"10.1142/s0218301324300091","DOIUrl":"https://doi.org/10.1142/s0218301324300091","url":null,"abstract":"The research paradigm in physics has evolved through three distinct phases: empirical observation and induction, theoretical modeling and deduction and computational numerical analysis and simulation. We are now situated within a novel epoch wherein the scientific research paradigm is increasingly shaped by the preeminence of large-scale data and artificial intelligence, particularly within the realm of AI for science applications. The advent of high-energy colliders coupled with Monte Carlo simulations has given rise to an unprecedented accumulation of data. Nested within this transformative research paradigm, machine learning and artificial intelligence technologies have been extensively harnessed for the analysis of these vast data sets. Within the domain of high-energy nuclear physics, two prevalent machine learning techniques have emerged: Bayesian analysis and deep learning. The former employs comprehensive fitting methodologies that compare extensive data sets against theoretical models, enabling the extraction of critical information pertaining to the initial nuclear structure, parton distributions, the equation of state governing hot and dense nuclear matter, and the transport coefficients of the quark–gluon plasma, among other parameters. Conversely, the latter capitalizes on the unparalleled pattern recognition capabilities of deep learning to discern robust features from high-dimensional raw data, specifically targeting individual physical parameters. This paper elucidates the fundamental principles of machine learning and delineates its potential to augment high-energy nuclear physics research endeavors.","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-10DOI: 10.1142/s0218301324500241
E. Ruziev, S. V. Artemov, O. Tojiboev
{"title":"ANC and SF of bound states in 15N→14C + p,16O→15N + p,19F→18O + p,32S→31P + p","authors":"E. Ruziev, S. V. Artemov, O. Tojiboev","doi":"10.1142/s0218301324500241","DOIUrl":"https://doi.org/10.1142/s0218301324500241","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140992913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.1142/s0218301324500198
Bao-Bao Jiao
{"title":"Evaluating nuclear charge radii based on the mean mass-density parameter using BP Neural Networks","authors":"Bao-Bao Jiao","doi":"10.1142/s0218301324500198","DOIUrl":"https://doi.org/10.1142/s0218301324500198","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140352893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.1142/s0218301324500186
Y. Berezhnoy, V. V. Pilipenko, G. Onyshchenko, P. E. Kuznietsov, I. Yakymenko, A. V. Anataichuk
{"title":"Analysis of elastic α58-Ni Scattering in the Energy Region 82-699 MeV by the S-Matrix Model","authors":"Y. Berezhnoy, V. V. Pilipenko, G. Onyshchenko, P. E. Kuznietsov, I. Yakymenko, A. V. Anataichuk","doi":"10.1142/s0218301324500186","DOIUrl":"https://doi.org/10.1142/s0218301324500186","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":"223 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-18DOI: 10.1142/s0218301324500174
Miao Li, Wen-Bo Ding, Jin-Ming Wei, Jin-Fang Du
{"title":"Effects of Free Parameterized TOV Equations on Properties of Neutron Stars with Hyperons","authors":"Miao Li, Wen-Bo Ding, Jin-Ming Wei, Jin-Fang Du","doi":"10.1142/s0218301324500174","DOIUrl":"https://doi.org/10.1142/s0218301324500174","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":"15 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-15DOI: 10.1142/s0218301324300017
M. Thoennessen
{"title":"2023 update of the discoveries of nuclides","authors":"M. Thoennessen","doi":"10.1142/s0218301324300017","DOIUrl":"https://doi.org/10.1142/s0218301324300017","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":"97 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.1142/s0218301324500162
E. Ummukulsu, Antony Joseph
{"title":"Shape evolution of thorium isotopes in between the drip-lines","authors":"E. Ummukulsu, Antony Joseph","doi":"10.1142/s0218301324500162","DOIUrl":"https://doi.org/10.1142/s0218301324500162","url":null,"abstract":"","PeriodicalId":508210,"journal":{"name":"International Journal of Modern Physics E","volume":"47 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：