arXiv subjects

Tiancong Wang

Publications and source records attributed to Tiancong Wang.

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↗

arXiv2025

Polarization Position Angle Swing and the Rotating Vector Model of Repeating Fast Radio Bursts

Fast radio bursts (FRBs), typically highly polarized, usually have a nearly constant polarization position angle (PA) during each burst. Some bursts show significant PA variations, and one of them was claimed to have a PA variation pattern consistent with the prediction of the rotating vector model (RVM) commonly adopted to fit the PA variations in radio pulsars. We systematically study the PA evolution pattern of 1727 bursts from three active repeating FRB sources monitored by the Five-hundred-meter Aperture Spherical Telescope (FAST). We identify 46 bursts whose PA variations are fully consistent with the RVM. However, the inferred geometrical parameters and rotation periods derived from these RVM fitting are inconsistent from each other. This suggests that the magnetosphere of the FRB central engine is constantly distorted by the FRB emitter, and the magnetic configuration is dynamically evolving.

astro-ph.HE↗

arXiv2020

Persistent Data Retention Models

Non-Volatile Memory devices may soon be a part of main memory, and programming models that give programmers direct access to persistent memory through loads and stores are sought to maximize the performance benefits of these new devices. Direct access introduces new challenges. In this work, we identify an important aspect of programming for persistent memory: the persistent data retention model. A Persistent Data Retention Model describes what happens to persistent data when code that uses it is modified. We identify two models present in prior work but not described as such, the Reset and Manual Model, and we propose a new one called the Automatic Model. The Reset model discards all persistent data when a program changes leading to performance overheads and write amplification. In contrast, if data is to be retained, the Manual Model relies on the programmer to implement code that upgrades data from one version of the program to the next. This reduces overheads but places a larger burden on the programmer. We propose the Automatic Model to assist a programmer by automating some or all of the conversion. We describe one such automatic approach, Lazily Extendible Data Structures, that uses language extensions and compiler support to reduce the effort and complexity associated with updating persistent data. We evaluate our PDRMs in the context of the Persistent Memory Development Kit (PMDK) using kernels and the TPC-C application. Manual Model shows an overhead of 2.90% to 4.10% on average, and LEDS shows overhead of 0.45% to 10.27% on average, depending on the workload. LEDS reduces the number of writes by 26.36% compared to Manual Model. Furthermore, LEDS significantly reduces the programming complexity by relying on the compiler to migrate persistent data.

cs.PL↗