arXiv subjects

Zelin Ren

Publications and source records attributed to Zelin Ren.

5 recordsLinked to original sources

arXiv2026

The detection prospects of the polarizations in the plateau phase of GRB afterglow by eXTP

Approximately (20-50)$\%$ of the gamma-ray burst (GRB) X-ray afterglows exhibit the shallow decay features. Two popular energy-injection models had been proposed to interpret such observational phenomenons, the relativistic wind bubble (RWB) model with a Poynting-flux injection and the structured ejecta (SE) model with a dynamical energy injection. Polarization predictions of the two models had been investigated and can be used as a test of the two models. However, the impacts of the parameters on the model predictions were not studied and the comparisons with the detection ability of the forthcoming mission, enhanced X-ray Timing and Polarimetry (eXTP), had not been discussed. We considered the above issues and found that influences of the model parameters on the predicted polarizations of the two models are very limited. To perform a feasible polarization detection during the plateau phase, the priority ToO response is required. The detection probability of the GRB plateau phase is about $1/3$ for one pointing under the priority ToO. The polarization detection probability would depend on the ratio between the Poynting-flux injection to the dynamical energy injection, which is unclear currently. The predicted flux density and polarization degree (PD) of the RWB model could be well above the threshold flux and minimal detectable polarization degree of the polarimetry focusing array (PFA) on board eXTP, while the predicted PDs of the SE model would be difficult to be detected by eXTP/PFA. Therefore, a detection of a significant polarization signal during the GRB plateau phase would prefer the RWB model and the injected energy would be in the form of the Poynting flux, while a non detection of the polarized signal would indicate a dynamical energy injection of the SE model.

astro-ph.HE↗

arXiv2026

Polarization of GRB standard X-ray afterglow and its detection prospects by eXTP

The polarization signatures of Gamma-ray Burst (GRB) afterglows serve as a powerful diagnostic tool for studying their environments and jet physics. This work systematically investigates the X-ray (2--8~keV) polarization properties of standard GRB afterglows and assesses their detectability with the Polarimetry Focusing Array aboard the enhanced X-ray Timing and Polarimetry (eXTP) satellite. A Morris global sensitivity analysis is first conducted to identify the dominant parameters, which are then assigned observationally motivated probability distributions. In particular, the isotropic energy, half-opening angle, and initial Lorentz factor are sampled jointly via a Gaussian copula to reproduce the empirical Ghirlanda and Liang correlations. Monte Carlo simulations of $10^{3}$ afterglows are performed and validated against the observed 10~keV flux distributions of a selected Fermi--Swift sample (K--S $p = 0.29$ at $10^{3}~\mathrm{s}$ and $p = 0.18$ at $10^{4}~\mathrm{s}$). The simulations yield an overall polarization event rate of $\lesssim 1.5\%$ for standard GRB X-ray afterglows with eXTP/PFA, reflecting the intrinsically low polarization produced by a random magnetic field confined to the shock plane. The optimal detection window occurs near the jet break at late times, when the PD peaks. For exceptionally luminous events such as GRB~221009A, however, the PD remains above the MDP over the full interval $10^{3}$--$10^{6}~\mathrm{s}$, demonstrating that eXTP/PFA can capture nearly the entire polarization evolution for such rare, bright bursts.

astro-ph.HE↗

arXiv2023

Exploring Interacting Dark Energy with Chaos Quantum-Behaved Particle Swarm Optimization

Models with an interaction between dark energy and dark matter have already been studied for about twenty years. However, in this paper, we provide for the first time a general analytical solution for models with an energy transfer given by $\mathcal{E} = 3H(ξ_1 ρ_c + ξ_2 ρ_d)$. We also use a new set of age-redshift data for 114 old astrophysical objects (OAO) and constrain some special cases of this general energy transfer. We use a method inspired on artificial intelligence, known as Chaos Quantum-behaved Particle Swarm Optimization (CQPSO), to explore the parameter space and search the best fit values. We test this method under a simulated scenario and also compare with previous MCMC results and find good agreement with the expected results.

astro-ph.CO↗

arXiv2023

A bias using the ages of the oldest astrophysical objects to address the Hubble tension

Recently different cosmological measurements have shown a tension in the value of the Hubble constant, $H_0$. Assuming the $Λ$CDM model, the Planck satellite mission has inferred the Hubble constant from the cosmic microwave background (CMB) anisotropies to be $H_0 = 67.4 \pm 0.5 \, \rm{km \, s^{-1} \, Mpc^{-1}}$. On the other hand, low redshift measurements such as those using Cepheid variables and supernovae Type Ia (SNIa) have obtained a significantly larger value. For instance, Riess et al. reported $H_0 = 73.04 \pm 1.04 \, \rm{km \, s^{-1} \, Mpc^{-1}}$, which is $5σ$ apart of the prediction from Planck observations. This tension is a major problem in cosmology nowadays, and it is not clear yet if it comes from systematic effects or new physics. The use of new methods to infer the Hubble constant is therefore essential to shed light on this matter. In this paper, we discuss using the ages of the oldest astrophysical objects (OAO) to probe the Hubble tension. We show that, although this data can provide additional information, the method can also artificially introduce a tension. Reanalyzing the ages of 114 OAO, we obtain that the constraint in the Hubble constant goes from slightly disfavoring local measurements to favoring them.

astro-ph.CO↗

arXiv2021

Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization

Kernel principal component analysis (KPCA) is a well-recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel trick-based method, KPCA inherits two major problems. First, the form and the parameters of the kernel function are usually selected blindly, depending seriously on trial-and-error. As a result, there may be serious performance degradation in case of inappropriate selections. Second, at the online monitoring stage, KPCA has much computational burden and poor real-time performance, because the kernel method requires to leverage all the offline training data. In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed. The core idea is to parameterize all feasible kernel functions using the novel nonlinear DAE-FE (deep autoencoder based feature extraction) framework and propose DAE-PCA (deep autoencoder based principal component analysis) approach in detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has more advantage in terms of automatic searching of the most suitable nonlinear high-dimensional space according to the inputs. Furthermore, the online computational efficiency improves by approximately 100 times compared with the conventional KPCA. With the Tennessee Eastman (TE) process benchmark, the effectiveness and superiority of the proposed method is illustrated.

cs.LG↗