高能暂现源 · 每日文献综述

Stellar rotation plays a key role in the transfer of angular momentum, and a large sample of Be-type stars with reliable projected rotational velocities is crucial for understanding their formation and evolution. In this work, we derive the projected rotational velocities ($v$\,sin\,$i$) of 479 Be-type stars using the Fourier transform method, based on their LAMOST Medium-resolution Survey (MRS) spectra. Our results suggest that the Fourier transform method can provide reliable $v$\,sin\,$i$ values for Be-type stars by analyzing the \ion{He}{1}\,lines at 4922, 5015, 5047, and 6678 \,Åin their LAMOST MRS spectra. A K-S test indicates that Be-type stars with different H$α$ emission line morphologies exhibit different $v$\,sin\,$i$ distributions, and Be-type stars with double-peaked emission have a higher fraction of rapid rotators than those with single-peak emission. The $v$\,sin\,$i$ distributions of our Be-type stars in the field, OB associations, and clusters show no significant differences. The deconvolved $v$\,sin\,$i$ distribution of our entire Be-type star sample does not exhibit a bimodal distribution but rather a single peak at $v\approx260$\,km$\cdot$s$^{-1}$. Based on the analysis of 105 stars in our sample, we find that the mean equatorial rotational velocity is 0.74 times the critical velocity. Furthermore, we investigate the relationship between $v$\,sin\,$i$ and the H$α$ peak separation velocity for Be-type stars exhibiting double-peak H$α$ emission lines, using Pearson and Spearman rank correlation coefficients.

Beam mismatch in polarization-sensitive bolometer pairs produces temperature-to-polarization (T → P) leakage that contaminates cosmic microwave background (CMB) polarization maps and biases constraints on the tensor-to-scalar ratio. Conventional deprojection methods rely on the specific Gaussian beam model and filter the time-ordered data, introducing some extra E → B mixing that must be corrected. In this work, we demonstrate that a deep learning approach can efficiently remove these systematics directly at the map level. Using an Ali CMB Polarization Telescope (AliCPT)-like telescope configuration and differential beam parameters drawn from BICEP/Keck measurements, we generate 4000 full-sky mock Q and U maps containing both the true primordial CMB signal and the T → P leakage induced by beam mismatch. We divide each full-sky map into 48 square patches of size 512 × 512 and consider 12 of them that enclose the observation field of AliCPT. For each of these square regions, a multi-patch hierarchical convolutional neural network based on the U-Net architecture is trained on 3600 randomly selected sky patches extracted from the contaminated maps; the corresponding uncontaminated patches serve as ground truth. On an independent test set, the trained network recovers the true polarization maps with a mean absolute deviation of 0.024 μK in Q and 0.023 μK in U. The reconstructed BB and EE angular power spectra match the input cosmology to within residuals of order 10<SUP>−4</SUP> μK<SUP>2</SUP> and 10<SUP>−3</SUP> μK<SUP>2</SUP>, respectively. Operating directly on maps, our neural network model avoids the filtering-induced E → B mixing and achieves millisecond-level inference time per sample, making it a promising alternative to traditional techniques under the simulation assumptions considered here and potentially applicable to next-generation CMB experiments such as AliCPT and LiteBIRD.