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Journal of Physics G: Nuclear and Particle Physics - latest papers

Latest articles for Journal of Physics G: Nuclear and Particle Physics

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  • Semi-classical and boson descriptions of scissors states
    A Hamiltonian with two interacting rotors is alternatively treated semi-classically and by a Dyson boson expansion method. The linearized equations of motion lead to a dispersion equation for the wobbling frequency. We define a ground band with energies consisting of a rotational part and one half of the vibrational wobbling energy. Adding to each state energy the corresponding wobbling quanta, one obtains the first excited band. Phonon amplitudes are used to calculate the reduced probability for the inter-band M1 transitions. The states exhibit a shears character. We point out a chiral symmetry that is broken by the interaction term, leading to a pair of twin chiral bands. Applications are made for 156Gd. We outline the ability of the two-rotor model to account for the wobbling and chiral motion in nuclei. Although the chosen trial function does not have a definite total angular momentum, for two particular ansatz of the pairs Ip, In, the average value of the total angular momentum approximates, to a certain accuracy, the partial angular momentum Ip. In this context, the rotational bands defined throughout this present paper could be labeled by the total I.

  • Search for 14.4 keV solar axions from the M1 transition of 57Fe using a dark matter direct detection public dataset
    In this work, a search for 57Fe 14.4 keV solar axions is conducted through the axion–electron and axion–photon coupling with the 1.16 tonne year public dataset of XENONnT. For the axion–electron coupling, we present the most stringent constraint on the product of the effective axion–nucleon coupling and the axion–electron coupling ( ) for axion mass MA below 14.4 keV. This result improves upon the previous best limit by approximately one order of magnitude. For the axion–photon coupling, the first dark matter direct detection (DMDD) experimental constraint on the product of the effective axion–nucleon coupling and the axion–photon coupling ( ) is derived through the inverse Primakoff effect. The result provides the most stringent limit for MA in 3.3 (4.6) × 10−5 to 1 × 10−3 keV, with a screening length r0 = 2.45 (0.49) Å. It sets the first limit for MA in 1.0 × 10−3–14.4 keV, which significantly extends the parameter space of ( ). This work highlights that DMDD experiments hold significant potential for future axion searches.

  • New physics effects in semileptonic B ¯ s ...
    We analyze the new physics (NP) effects in semileptonic decay induced by the b → uℓνℓ quark level transition. We consider the vector, scalar and tensor NP Lorentz structures in addition to the standard model in effective field theory approach. Constraints on NP parameters are obtained from experimental observations of both semileptonic and leptonic decays of B mesons, which are governed by the underlyng b → uℓνℓ transition. We explore the NP effects in differential branching fraction, lepton forward–backward asymmetry, fraction of longitudinal polarization of K* meson and normalized angular observables in decay.

  • Criticality analysis of nuclear binding energy neural networks
    Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

  • Design and performance simulation of a laser Compton scattering gamma-ray source for the HEPS facility
    An energy-tunable and monoenergetic laser Compton scattering gamma-ray source is designed to couplement the High Energy Photon Source at the Institute of High Energy Physics. This source is intended to provide high-quality γ-ray beams for photon-nuclear physics, nuclear astrophysics, and related applications. Its performance was evaluated using a two-laser system (CO2 laser and a fiber laser) through detailed Geant4 simulations. Results demonstrate that the designed γ-ray source can produce gamma rays with a wide energy (1–566 MeV), a high flux (on the order of 108–1010 counts s−1) and an excellent energy resolution of 0.39%–10%.