arXiv subjects

Dejia Zhou

Publications and source records attributed to Dejia Zhou.

4 recordsLinked to original sources

arXiv2026

FAST Discovery of $\mu$Jy Radio Pulsations from PSR J2238+5903, Providing a DM Distance Anchor for the Candidate TeV Halo 1LHAASO J2238+5900

We report the first detection of radio pulsations from PSR J2238+5903, a gamma-ray pulsar spatially coincident with the extended TeV source 1LHAASO J2238+5900. Our 3000 s FAST L-band observation reveals a weak periodic signal at the known Fermi-LAT spin period, with $P=162.76568$ ms and $\mathrm{DM}=247.5\pm3.0~\mathrm{pc~cm^{-3}}$. The signal is independently confirmed by both FFT-based and Fast Folding Algorithm searches. The radiometer equation gives a flux density of $S_{1250}\simeq3\,\mu$Jy, placing PSR J2238+5903 among the faintest radio-detected Fermi pulsars. Interpreting the DM with Galactic electron-density models gives $d_{\rm DM}=7.4\pm3.9$ kpc. At this distance, the LHAASO WCDA 39\% containment radius corresponds to a characteristic diameter of $\sim132$ pc, and the $>1$ TeV luminosity is $L_{\rm TeV}\simeq7.1\times10^{34}$ erg s$^{-1}$, about 8\% of the pulsar's spin-down power. The radio DM thus provides the first pulsar-specific distance constraint for assessing whether 1LHAASO J2238+5900 is a young relic-PWN / TeV-halo transition system.

astro-ph.HE↗

arXiv2026

A Python/CuPy Software Correlator for QUEST: Real-Time Performance and Initial Imaging

We present a Python/CuPy FX software correlator for small radio interferometer arrays and evaluate it on QUEST (Qilu University Explorer Survey Telescope). The system combines multi-threaded data ingest, pinned-memory host-device transfers, GPU-accelerated correlation, Polyphase Filter Bank channelization, MAD-based RFI flagging, and delay/phase calibration in a single workflow aimed at array commissioning. On a single NVIDIA RTX 4090D GPU, the implementation reaches a peak throughput of 1.51 GB/s, which is sufficient for real-time operation in the four-antenna mode tested here. After calibration, the visibility phase across a clean 1.32-1.38 GHz band is flattened to a residual scatter of a few degrees. Using the calibrated visibilities, we form a four-antenna synthesis image of Cassiopeia A; the CLEANed image recovers a compact source at the phase center and reduces image-domain background fluctuations from order 0.1 to a few 0.01 Jy/beam. These results indicate that the software is suitable for small-array commissioning and initial synthesis imaging on QUEST. A GNSS-based beam measurement is included as a supporting commissioning check.

astro-ph.IM↗

arXiv2025

ASTROFLOW: A Real-Time End-to-End Pipeline for Radio Single-Pulse Searches

Fast radio bursts (FRBs) are extremely bright, millisecond duration cosmic transients of unknown origin. The growing number of wide-field and high-time-resolution radio surveys, particularly with next-generation facilities such as the SKA and MeerKAT, will dramatically increase FRB discovery rates, but also produce data volumes that overwhelm conventional search pipelines. Real-time detection thus demands software that is both algorithmically robust and computationally efficient. We present Astroflow, an end-to-end, GPU-accelerated pipeline for single-pulse detection in radio time-frequency data. Built on a unified C++/CUDA core with a Python interface, Astroflow integrates RFI excision, incoherent dedispersion, dynamic-spectrum tiling, and a YOLO-based deep detector. Through vectorized memory access, shared-memory tiling, and OpenMP parallelism, it achieves 10x faster-than-real-time processing on consumer GPUs for a typical 150 s, 2048-channel observation, while preserving high sensitivity across a wide range of pulse widths and dispersion measures. These results establish the feasibility of a fully integrated, GPU-accelerated single-pulse search stack, capable of scaling to the data volumes expected from upcoming large-scale surveys. Astroflow offers a reusable and deployable solution for real-time transient discovery, and provides a framework that can be continuously refined with new data and models.

astro-ph.IM↗

arXiv2025

Supernova Classification using the Recurrent Neural Network in the CSST Ultra-Deep Field Survey

We study supernova (SN) classification using the Recurrent Neural Networks (RNNs) within the Chinese Space-station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey and explore the improvements in cosmological constraints. We simulate Type Ia supernovae (SNe Ia) and core-collapse supernovae (CCSNe) using SNCosmo with SALT3 SN Ia model and CCSN templates, and apply the SuperNNova (SNN) program for classification. Our study indicates that the SNN combined with the Joint Light-curve Analysis cuts can enhance the purity of the CSST-UDF SN Ia sample up to over 99.5% with 2,193 SNe Ia and 4 CCSNe, which can significantly increase the reliability of the cosmological constraints. The method based on the Bayesian Estimation Applied to Multiple Species with Bias Corrections framework is used to correct the SN Ia magnitude bias caused by the selection effect and CCSN contamination, and the Markov Chain Monte Carlo (MCMC) method is employed for cosmological constraints. We find that the accuracy of the constraints on the matter density $\Omega_{\rm M}$ and the equation of state of dark energy parameter $w$ can achieve 14% and 18%, respectively, assuming the flat $w$CDM model. This result is comparable to current surveys relying on spectroscopic confirmation. Our results indicate that our data analysis method is effective, and the CSST-UDF SN photometric survey is a powerful tool to explore the expansion history of the Universe.

astro-ph.CO↗