Highlights
  • Tilted-axis-cranking covariant density functional theory for high-spin spectroscopy of 69Ga
    The tilted-axis-cranking covariant density functional theory is applied to investigate the three newly-observed positive-parity bands SI, SII, and SIII in 69Ga. The energy spectra and angular momenta are calculated, and they agree closely with experimental data. For band SI, pairing correlations are crucial for the states with spin $I\leq 23/2\hbar$. Bands SII and SIII are suggested to be signature partner bands with positive and negative signatures, respectively. By analyzing the angular momentum alignments, we reveal that the $g_{9/2}$ protons and neutrons are crucial in the collective structures of 69Ga. The transition probabilities $B(E2)$ for these bands are predicted, awaiting further experimental verification.
  • Null test of cosmic curvature using deep learning method
    Determining the spatial curvature of the Universe, a fundamental parameter defining the global geometry of spacetime, remains crucial yet contentious due to existing observational tensions. Although Planck satellite measurements have provided precise constraints on spatial curvature, discrepancies persist regarding whether the Universe is flat or closed. Here, we introduce a model-independent approach leveraging deep learning techniques, specifically residual neural networks (ResNet), to reconstruct the dimensionless Hubble parameter E(z) and the normalized comoving distance D(z) from H(z) data and multiple SNe Ia compilations. Our dual-block ResNet architecture, which integrates a model-driven block informed by $ \Lambda $CDM and a purely data-driven block, yields smooth and robust reconstructions and enables the derivation of D'(z). By combining these reconstructed quantities, we assess the curvature diagnostic function $ {\cal{O}}_k(z) $. Analyses of the Pantheon+ sample support spatial flatness at the 1$ \sigma $ level over 0 < z < 2.5, with a mild tendency toward negative curvature at high redshift. Reconstructions based on Union3 and DESY5, however, show stronger departures toward negative curvature at intermediate and high redshifts. These results highlight the need for expanded and refined observational datasets to conclusively resolve these tensions and comprehensively investigate cosmic geometry.
  • Extracting the kinetic freeze-out properties of high energy pp collisions at the LHC with event shape classifiers
    Event shape measurements are crucial for understanding the underlying event and multiple-parton interactions (MPIs) in high energy proton-proton (pp) collisions. In this study, the Tsallis blast-wave model with independent non-extensive parameters for mesons and baryons was applied to analyze the transverse momentum spectra of charged pions, kaons, and protons in pp collision events at $ \sqrt{s}=13 $ TeV classified by event shape estimators such as relative transverse event activity, unweighted transverse spherocity, and flattenicity. Our analysis reveals consistent trends in the kinetic freeze-out temperature and non-extensive parameter across different collision systems and event shape classes. The use of diverse event-shape observables in pp collisions has significantly expanded the accessible freeze-out parameter space, enabling a more comprehensive exploration of its boundaries. Among these event shape classifiers, flattenicity emerges as a unique observable for disentangling hard process contributions from additive MPI effects, which helps isolate collective motion effects encoded by the radial flow velocity. Through the analysis of the interplay between event-shape measurements and kinetic freeze-out properties, we gain deeper insights into mechanisms responsible for flow-like signatures in pp collisions.
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