arXiv subjects

Chengwei Liang

Publications and source records attributed to Chengwei Liang.

4 recordsLinked to original sources

arXiv2026

No Strong Evidence for Plasma Lensing in FRB 20240114A

FRB~20240114A is an extremely active repeating fast radio burst for which plasma lensing has been proposed to explain its burst-rate variations, spectral evolution, and apparently ``carbon-copy'' burst pairs. Using FAST data and publicly available Parkes observations, we test this interpretation with a one-dimensional Gaussian plasma-lens model. Although the burst-rate enhancements can be fitted separately, the corresponding magnification peaks and demagnification troughs are offset by far more than predicted and show no consistent periodicity. Moreover, with more than 10,000 bursts detected, a few apparently ``carbon-copy'' pairs can readily occur by chance. The burst bandwidth is not systematically narrower during the proposed lensing interval, nor are the burst energies significantly enhanced during the predicted magnification interval. These results provide no compelling evidence that a single Gaussian plasma lens explains the observed variability, which is more likely dominated by intrinsic source activity.

astro-ph.HE↗

arXiv2026

Random Polarization Position Angle Behaviors across Bursts of Repeating Fast Radio Bursts

Fast radio bursts (FRBs), highly polarized, mostly have a nearly constant polarization position angle (PA) during each burst. Their PAs are observed to vary from burst to burst, with the statistical properties remaining stable across different observation sessions. We found that the intrinsic PAs of repeating FRBs are approximately Gaussian distributed, suggesting that the emission likely originates from a localized region within the neutron star's magnetosphere. A periodicity search of the PA time series using the Lomb-Scargle periodogram reveals no credible periodic signal in the period range from 10 ms to $10^7$ ms, and similar analyses of several active observations also yield null detections. We interpret these properties by extending the rotating vector model to include a dynamically evolving magnetosphere, in which the effective magnetic axis varies from burst to burst due to stochastic perturbations. In this framework, the observed PA distributions can naturally arise from geometric projection effects, and the absence of periodicity reflects the random wandering of the magnetic axis within a confined region. This scenario provides a natural explanation for both repeating and apparently non-repeating FRBs.

astro-ph.HE↗

arXiv2026

Differences in Text Generated by Diffusion and Autoregressive Language Models

Diffusion language models (DLMs) are promising alternatives to autoregressive language models (ARMs), yet the intrinsic differences in their generated text remain underexplored. We first find empirically that off-the-shelf DLMs exhibit lower $n$-gram entropy, higher semantic coherence, and higher semantic diversity. To understand the cause, we conduct controlled experiments that decouple the effects of training objectives and decoding algorithms. Results suggest that the DLM training objective contributes to the increases in semantic coherence and semantic diversity, but has a minor influence on entropy. These differences are primarily driven by the bidirectional context; other components in the training objective, such as input masking, label masking, and the weighting function, have a much weaker influence. Further, our experiments demonstrate that the reduction in entropy stems from DLMs' decoding algorithms, particularly confidence-based remasking strategies. We provide a theoretical understanding for this entropy reduction phenomenon. Together, our work uncovers key mechanisms underlying the differences between DLMs and ARMs in text generation, and informs future design of training objectives and decoding algorithms in DLMs.

cs.CL↗

arXiv2025

Towards DM-free search for Fast Radio Bursts with Machine Learning -- I. An implementation on multibeam data

Searching for fleeting radio transients like fast radio bursts (FRBs) with wide-field radio telescopes has become a common challenge in data-intensive science. Conventional algorithms normally cost enormous time to seek candidates by finding the correct dispersion measures, of which the process is so-called dedispersion. Here we present a novel scheme to identify FRB signals from raw data without dedispersion using Machine Learning (ML). Under the data environment for multibeam receivers, we train the EfficientNet model and achieve both exceeding 92% accuracy and precision in FRB recognition. We find that the searching efficiency can be significantly enhanced without the procedure of dedispersion compared with conventional softwares like TransientX and presto. Specifically, the impact of radio frequency interference (RFI) for single-beam and multibeam data has been investigated, and we find ML can naturally mitigate RFI under the multibeam environment. Finally, we validate the trained model on actual data from the current FRB surveys carried out by the Five-hundred-meter Aperture Spherical radio Telescope, which provides considerable potential for real implementation in the future.

astro-ph.IM↗