Pairing correlations and eigenvalues of two-body density matrix in atomic nuclei

Sambataro, M.; Sandulescu, N.

doi:10.1016/j.aop.2019.168061

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…Unsupervised learning can be used to discover correlations in calculations of nuclear wave functions that use a microscopic Hamiltonian. There are terabytes of data from calculations with nucleonic degrees of freedom that can be data mined to discover emergent phenomena such as clustering [67-70], superfluidity [71], and nuclear rotation [72].…”

Section: Development Of a Spectroscopic-quality Nuclear Energy Densit...mentioning

confidence: 99%

A.I. for nuclear physics

et al. 2021

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Computational techniques lay at the center of accelera-642 tor design. Modern simulation codes are capable of self-643 consistent tracking 10 9 charged particles through complex, 644 nonlinear external field environments, and in modeling inter-645 actions with materials. Highly developed and benchmarked 646 engineering codes are employed to design and optimize 647 acceleration structures, high power beam targets, vacuum 648 systems, plasma and solid-state devices for instrumentation. 649 ML/AI techniques are coming into common use during the 650 design stage to facilitate studies of complex beam dynamics 651 in search of optimum lattices and working point tunes, to 652 study novel schemes for cooling hadron beams, to improve 653 diagnostic schemes for beam measurements, to create per-654 formance gains in high intensity and high brightness beam 655 sources, to name but a few [76]. 656 Reinforcement learning and Bayesian optimization are 657 techniques that can be used to explore large design parame-658 ter spaces. However, in order for these techniques to provide 659 reliable and optimal solutions they need to be configured and 660 tuned for the specific application. An incorrect kernel selec-661 tion used in a Gaussian Process technique can lead to disas-662 trous results. Similarly, using a sub-optimal search strategy 663 and/or policy model architecture in reinforcement learning 664 will converge to sub-optimal result. Therefore, it's critical to 665 build or leverage a framework, such as CANDLE and ExaRL, 666 to improve the chances of an optimal solution.667 4.3.2 Provenance and prognostication for accelerator 668 sub-systems 669 Scientific productivity at accelerator-based NP facilities is 670 directly impacted by unscheduled losses of beam time. The 671 trip rate (see Fig. 3) is attributable to multiple causation fac-672 tors that vary in frequency and severity. Some of the main 673 causes are due to excessive beam losses detected by the 674 Machine Protection System (MPS) and to loss of RF cavity 675 allocated to each experiment. Ideally, this holistic approach 830 can be applied to the design of the experiment itself by opti-831 mizing machine and detector properties as a single system. 832 Experiment design not limited by computation Future 833 experimental advances in accelerator-based NP research 834 hinges on increased luminosity, which provides the statistics 835 necessary to observe rare processes. ML methods will reduce 836 computational barriers to this goal. Intelligent decisions 837 about data storage is required to ensure the relevant physics 838 is captured. Depending on the experiment, AI can improve 839 the data taken through data compactification, sophisticated 840 triggers (both software and hardware-based), and fast-online 841 analysis. 842 An example would be the incorporation of neural networks 843 in the FPGAs which comprise the front-end triggers of com-844 plex experiments. The very large channel counts afforded by 845 modern semiconductor detectors combined with high beam 846 lumino...

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Section: Development Of a Spectroscopic-quality Nuclear Energy Densit...mentioning

confidence: 99%

A.I. for nuclear physics

et al. 2021

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Classification of pairing phase transition in metal clusters in vanadium and aluminum particles using Grossman theory

Elahizadeh,

Zarifi,

Dehghani

et al. 2024

Physica B: Condensed Matter

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Searching for high-temperature superconductivity: from Mendeleev to Seiberg–Witten via Madelung and beyond

Kholodenko

2022

Progress of Theoretical and Experimental Physics

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Recently, noticeable progress has been achieved in the area of high-temperature superconductors. Maximum temperatures Tc of 250 K (−23○ C) for LaH10 and 288 K (+15○ C) for CSH8 have been reported at megabar pressures. The highest possible Tcs were achieved by employing hydrides of chemical elements. Empirically, many of these are made of Madelung-exceptional atoms. Here, the theoretical background is provided to explain this observation. The, thus far empirical, Madelung rule controls Mendeleev’s law of periodicity. Although the majority of elements do obey this rule, there are some exceptions. Thus, it is of interest to derive it and its exceptions theoretically in view of experimental findings. As a by-product, such a study yields a plausible explanation of the role of Madelung-exceptional atoms in the design of high-Tc superconductors. Thus far the atoms obeying the Madelung rule and its exceptions have been studied with help of relativistic Hartree–Fock calculations. In this work we reobtain both the rule and the exceptions analytically. The newly developed methods are expected to be of value in quantum many-body theory and, in particular, in the theory of high-Tc superconductivity. Ultimately, the new methods involve some uses of the Seiberg–Witten theory known as the extended Ginzburg–Landau theory of superconductivity. Using results of Sieberg–Witten theory, the difference between Madelung-regular and Madelung-exceptional atoms is explained in terms of the topological transition. The extension of this single-atom result to solids of the respective elements is also discussed.

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Pairing correlations and eigenvalues of two-body density matrix in atomic nuclei

Cited by 4 publications

References 38 publications

A.I. for nuclear physics

A.I. for nuclear physics

Classification of pairing phase transition in metal clusters in vanadium and aluminum particles using Grossman theory

Searching for high-temperature superconductivity: from Mendeleev to Seiberg–Witten via Madelung and beyond

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