Universe

This study presents Solar Cycle (SC) 25 (2020–2025) results from the homogeneous Kanzelhöhe Solar Observatory (KSO) catalogue project and completes the analysis of SCs 19–25. Only SC18 remains to be processed before the catalogue is finalized. Using KSO white-light observations, we determined sidereal rotation velocities and solar differential rotation (DR) parameters A and B from day-to-day positional changes in sunspot-groups and from robust linear fits to their position–time measurements. Their temporal evolution and relationship with solar activity were analyzed using yearly mean Solar Index and Long-term Solar Observations (SILSO) sunspot numbers. Rotation velocity residuals were also analyzed as a function of time and heliographic latitude in search of possible torsional oscillation (TO) signatures. The obtained DR parameters are consistent with previously published results. For SC25, a strong anticorrelation between the yearly fitted parameters A and B was found, in agreement with earlier studies, although its interpretation is affected by the covariance between the two parameters introduced by the fitting procedure. The residual rotation velocities exhibit temporal behaviour similar to that reported in previous sunspot-group studies. Latitude–time residual maps reveal patterns that may represent TO signatures, however the limited temporal coverage of SC25, the characteristics of sunspot group tracers, and the use of interpolated data require a cautious interpretation.

We consider the effects of radiative corrections in Starobinsky-like models of inflation, concentrating on models of inflation formulated in $N=1$ no-scale supergravity. Inflaton couplings with matter fields are necessary for reheating and these have an impact on loop corrections due to the inflaton potential. Whilst corrections due to the supergravity couplings of the inflaton to Standard Model (MSSM) fields are negligible, we use Planck data to obtain interesting constraints on GUT bilinear couplings, vevs, and gauge boson masses that could be sharpened by future CMB measurements.

We present GalSpecCNN, a probabilistic deep-learning framework for estimating fiber-scale stellar mass ($M_{*}$) and specific star formation rate (sSFR) from LAMOST galaxy spectra. The model uses a one-dimensional convolutional neural network to extract spectral features, followed by a probabilistic regression head that predicts a diagonal bivariate Gaussian distribution. Aleatoric uncertainty is modeled through the predicted variances, while epistemic uncertainty is approximated using Monte Carlo Dropout. On the test set, GalSpecCNN achieves residual scatters of 0.226 dex for $M_{*}$ and 0.319 dex for sSFR, with small mean residuals. The empirical coverages of the nominal 68% predictive intervals are 68.60% for $M_{*}$ and 71.58% for sSFR, indicating good overall calibration with mildly conservative coverage for sSFR. We apply the trained model to approximately $1.16\times {10}^5$ LAMOST DR7 galaxy spectra and construct a catalog of $M_{*}$ and sSFR estimates, together with their associated predictive intervals. This work provides a practical framework for uncertainty-aware estimation of galaxy physical properties from large spectroscopic surveys.