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  • Detecting cosmological phase transitions with Taiji: sensitivity analysis and parameter estimation
    2025, 49(10): 105103-105103-12. doi: 10.1088/1674-1137/ade65f
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    We investigate the capability of the Taiji space-based gravitational wave observatory to detect stochastic gravitational wave backgrounds produced by first-order phase transitions in the early universe. Using a comprehensive simulation framework that incorporates realistic instrumental noise, galactic double white dwarf confusion noise, and extragalactic compact binary backgrounds, we systematically analyze Taiji's sensitivity across a range of signal parameters. Our Bayesian analysis demonstrates that Taiji can robustly detect and characterize phase transition signals with energy densities exceeding $\Omega_{\text{PT}} \gtrsim 1.4 \times 10^{-11}$ across most of its frequency band, with strong sensitivity at approximately $10^{-3}$ to $10^{-2}$ Hz. For signals with amplitudes above $\Omega_{\text{PT}} \gtrsim 1.1 \times 10^{-10}$, Taiji can determine the peak frequency with relative precision better than 10%. These detection capabilities would enable Taiji to probe electroweak-scale phase transitions in various beyond-Standard-Model scenarios, potentially revealing new physics connected to baryogenesis and dark matter production. We quantify detection confidence using both Bayes factors and the Deviance Information Criterion, obtaining consistent results that validate our statistical methodology.
  • Nuclear mass predictions with a Bayesian neural network
    2025, 49(10): 104106-104106-7. doi: 10.1088/1674-1137/ade958
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    The Bayesian neural network (BNN) has been widely used to study nuclear physics in recent years. In this study, a BNN was applied to optimize seven theoretical nuclear mass models, namely, six global models and one local model. The accuracy of these models in describing and predicting masses of nuclei with both the proton number and the neutron number greater than or equal to eight was improved effectively for two types of numerical experiments, particularly for the liquid drop model and the relativistic mean-field theory, whose root mean square deviations (RMSDs) for describing (predicting) nuclear masses were reduced by 81.5%−90.6% (66.9%−84.2%). Additionally, the relatively stable RMSDs as nuclei move away from the β-stability line and the good agreement with experimental single-neutron separation energies further confirm the reliability of the BNN.
  • Exploring axion-like particle from observation of FSRQ Ton 599 by Fermi-LAT
    2025, 49(10): 105107-105107-9. doi: 10.1088/1674-1137/ade6d2
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    High energy photons traveling through astrophysical magnetic fields have the potential to undergo oscillations with axion-like particles (ALPs), resulting in modifications to the observed photon spectrum. High energy $ \gamma $-ray sources with significant magnetic field strengths provide an ideal environment to investigate this phenomenon. Ton 599, a flat spectrum radio quasar with a magnetic field strength on the order of Gauss in its emission region, presents a promising opportunity for studying ALP-photon oscillations. In this study, we analyze the effects of ALP-photon oscillations on the γ-ray spectrum of Ton 599, as observed by Fermi-LAT. Our investigation considers the potential influences of the broad-line region and dusty torus on the $ \gamma $-ray spectrum of Ton 599. We set the constraints on the ALP parameters at a 95% confidence level and show that the constraints on $ g_{a\gamma} $ can reach approximately $ 2 \times 10^{-12}\; \text{GeV}^{-1} $ for $ m_a \sim 10^{-9}\; \text{eV} $.
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ISSN 1674-1137 CN 11-5641/O4

Original research articles, Ietters and reviews Covering theory and experiments in the fieids of

  • Particle physics
  • Nuclear physics
  • Particle and nuclear astrophysics
  • Cosmology
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