arXiv subjects

Chun-Yan Jiang

Publications and source records attributed to Chun-Yan Jiang.

4 recordsLinked to original sources

arXiv2026

Self-Consistent Parker Bound on Magnetic Monopoles

Magnetic monopoles arise generically in unified theories and offer a natural explanation of charge quantization. Beyond collider searches and cosmic-ray experiments, their flux is constrained by Parker-type bounds requiring galactic magnetic fields to survive monopole energy extraction. We formulate a self-consistent Parker bound anchored in the lowest eigenmode of the galactic mean-field dynamo and convert the resulting limit to the present-day flux. Small-scale turbulent fields both seed this eigenmode and set the monopole velocity via stochastic acceleration before energy extraction from the coherent field. These unavoidable effects substantially modify the standard extended Parker bound at low and intermediate masses, yielding flux limits robust to primordial magnetic fields (PMFs); PMFs strong enough to alter these limits lie in regimes constrained by Ly$\alpha$ data or testable by 21-cm observations and other cosmological probes.

hep-ph↗

arXiv2026

Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection

Gravitational-wave astronomy has opened a direct observational window onto compact-object dynamics, strong-field gravity, and cosmology. Among the transient sources accessible through this window, core-collapse supernovae (CCSNe) are uniquely valuable because their signals can probe the engine of stellar collapse, proto-neutron-star dynamics, and explosion asymmetries, yet their weak, stochastic, and model-dependent waveforms remain difficult to detect. In this work, we develop a contrastive self-supervised convolutional autoencoder (CS-CAE) for CCSNe gravitational-wave signal detection. The method combines a convolutional autoencoder (CAE), a noise-centered latent regularizer, and a projection head trained with a contrastive objective. This design encourages independent noisy realizations of the same CCSNe signal to be mapped to nearby latent representations, thereby reducing the influence of random noise fluctuations. CS-CAE achieves performance comparable to a supervised convolutional neural network while clearly outperforming a conventional CAE baseline, and generalizes better to unseen numerical CCSNe waveform families. Under the Einstein Telescope (ET) detector configuration, the method achieves an effective sensitive distance of approximately 120 kpc and shows improved separation of CCSNe signals from stationary noise and transient glitches in the low-false-alarm regime. These results highlight the potential of CS-CAE as a robust and less template-dependent framework for CCSNe gravitational-wave searches.

gr-qc↗

arXiv2025

Mock Observations for the CSST Mission: Multi-Channel Imager--Instrument Simulation

The Chinese Space Station Survey Telescope (CSST), a two-meter aperture astronomical space telescope under China's manned space program, is equipped with multiple back-end scientific instruments. As an astronomical precision measurement module of the CSST, the Multi-Channel Imager (MCI) can cover a wide wavelength range from ultraviolet to near-infrared with three-color simultaneous high-precision photometry and imaging, which meets the scientific requirements for various fields. The diverse scientific objectives of MCI require not only a robust airborne platform, advanced optical systems, and observing facilities but also comprehensive software support for scientific operations and research. To this end, it is essential to develop realistic observational simulation software to thoroughly evaluate the MCI data stream and provide calibration tools for future scientific investigations. The MCI instrument simulation software will serve as a foundation for the development of the MCI data processing pipeline and will facilitate improvements in both hardware and software, as well as in the observational operation strategy, in alignment with the mission's scientific goals. In conclusion, we present a comprehensive overview of the MCI instrument simulation and some corresponding performances of the MCI data processing pipeline.

astro-ph.IM↗

arXiv2016

Lyman-Alpha Emitter Galaxies at z ~ 2.8 in the Extended Chandra Deep Field-South: I. Tracing the Large-Scale Structure via Lyman-Alpha Imaging

We present a narrowband survey with three adjacent filters for z=2.8--2.9 Lyman Alpha Emitter (LAE) galaxies in the Extended Chandra Deep Field South (ECDFS), along with spectroscopic followup. With a complete sample of 96 LAEs in the narrowband NB466, we confirm a large-scale structure at z~ 2.8. Compared to the blank field in NB470 and NB475, the LAE density excess in the NB466 field is ~6.0+/-0.8 times the standard deviation expected at z~2.8, assuming a linear bias of 2. The overdense large scale structure in NB466 can be decomposed into 4 protoclusters, whose overdensities are 4.6 - 6.6. These 4 protoclusters are expected to evolve into a Coma-like cluster at z~ 0. In the meanwhile, we investigate the average star-formation rates derived from Lyα, rest-frame UV and X-ray, the Lyα luminosity functions, the Lyα photon densities and their dependence on the environment. We find that the Lyα photon density in the overdense field (NB466) is ~50\% higher than that in the blank field (NB470+NB475). The 3 brightest LAEs, including a quasar at z=2.81, are all detected in X-ray and in NB466. These three LAE-AGNs contribute an extra 20--30\% Lyα photon density. Furthermore, we find that LAEs in overdense regions are younger and less dusty. We conclude that the structure we found is a significant and rare density peak, and narrowband imaging is an efficient method to detect and study such structures in the high-z universe.

astro-ph.CO↗