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Nuclear Theory·nucl-th

Thursday·February 1, 2024

5 papers—2 primary·3 cross-listed

All5Primary2Cross-lists3
  1. 01

    [Submitted on 30 Jan 2024]

    Tractable -matix model for reaction processes in muon catalyzed fusion

    Qian Wu · Masayasu Kamimura

    Reaction processes in muon catalyzed fusion (CF), or in the D-T mixture was comprehensively studied by Kamimura, Kino and Yamashita [Phys. Rev. C 107, 034607 (2023)] by solving the - coupled channel (CC) Schrödinger equation under a boundary condition where the muonic molecule was set as the initial state and the outgoing wave was in the channel. We approximate this CC framework and propose a considerably more tractable model using the -matrix method based on the Lippmann-Schwinger equation. Nuclear interactions adopted in the -matrix model are determined by reproducing the cross section of the reaction at low energies. The cross section of the strong-coupling rearrangement reaction is presented in a simple closed form based on our new model. This -matrix model have reproduced most of the calculated results on the above CF reaction reported by Kamimura et al. (2023) and is applicable to other CF systems such as , , , .

    Comments:
    11 pages, 11 figures
    Subjects:
    Nuclear Theory (nucl-th); Nuclear Experiment (nucl-ex); physics.atm-clus (physics.atm-clus)
    arXiv:
    2401.17358 [pdf]
    PRC(2024)·7 citations
  2. 02

    [Submitted on 31 Jan 2024]

    Second-order optimisation strategies for neural network quantum states

    M. Drissi · J. W. T. Keeble · J. Rozalén Sarmiento · A. Rios

    The Variational Monte Carlo method has recently seen important advances through the use of neural network quantum states. While more and more sophisticated ansätze have been designed to tackle a wide variety of quantum many-body problems, modest progress has been made on the associated optimisation algorithms. In this work, we revisit the Kronecker-Factored Approximate Curvature, an optimiser that has been used extensively in a variety of simulations. We suggest improvements on the scaling and the direction of this optimiser, and find that they substantially increase its performance at a negligible additional cost. We also reformulate the Variational Monte Carlo approach in a game theory framework, to propose a novel optimiser based on decision geometry. We find that, on a practical test case for continuous systems, this new optimiser consistently outperforms any of the KFAC improvements in terms of stability, accuracy and speed of convergence. Beyond Variational Monte Carlo, the versatility of this approach suggests that decision geometry could provide a solid foundation for accelerating a broad class of machine learning algorithms.

    Comments:
    35 pages, 9 figures, 4 tables. Accepted in Phil. Trans. R. Soc. A
    Subjects:
    Nuclear Theory (nucl-th); Quantum Physics (quant-ph)
    arXiv:
    2401.17550 [pdf]
    Phil.Trans.Roy.Soc.Lond.A(2024)·7 citations

Affiliations

first authorsco-authorsvia INSPIRE