Department of Education of Guangdong Province

Abstract We present a comprehensive multiband optical monitoring and spectral analysis of the blazar OJ 287, conducted using the 1.26 m National Astronomical Observatory–Guangzhou University Infrared/Optical telescope (NGT). During an intensive 17 day observational campaign from 2022 December to 2023 January, we obtained 2716 high-quality photometric observations (929 in the g band, 887 in the r band, and 900 in the i band). The data reveal significant variability across all bands. Our main findings are as follows. (1) Significant magnitude variations were detected: Δ m g = 0.945 ± 0.048 mag ( g band), Δ m r = 0.648 ± 0.023 mag ( r band), and Δ m i = 0.945 ± 0.051 mag ( i band). (2) Numerous intraday variability (IDV) events were identified, with characteristic parameters (Δ m , Δ T , and V R = Δ m Δ T ) showing consistent patterns between the g and r bands but distinct behavior in the i band. (3) Successful modeling of intraday light curves using a multi-Gaussian function, yielding brightening ( Δ T b ′ ) and dimming ( Δ T d ′ ) timescales, and revealing an average peak separation of Δ Tc = 42.96 ± 0.99 minutes that appears to be frequency independent. (4) Development of a novel radiation framework suggesting that the observed IDV phenomena originate from multiple components—with the frequency-independent variations attributable to the central black hole, while frequency-dependent variations stem from jet and accretion disk processes.

Abstract As the only commercial available thermoelectric material, Bi 2 Te 3 ‐based alloys offer exceptional near‐room temperature performance, while it is complicated to realize further improvement. Incorporating magnetic impurity is an effective strategy to decouple the relationship between thermal and electrical transport for improved TE performance, while the design of magnetic impurity with precisely tailored chemical components, size, distribution, and crystallinity remains a big challenge. Herein, the amorphous FeO x layers with weak ferromagnetism are introduced to the grain boundaries of commercial p‐type Bi 0.5 Sb 1.5 Te 3 materials to improve its TE performance. The special serrated shape and weak‐ferromagnetism of the FeO x layer promote high mobility and Seebeck coefficients of the ALD coated samples. The phonon scattering at the FeO x layer reduce lattice thermal conductivity by over 30%. Consequently, the optimized sample achieves the maximum and average ZT of 1.42 at 330 K and 1.1 within 300–525 K, respectively, marking increases of 43.7% and 37.5% compared to the matrix. This work delves into the role of thermo‐electro‐magnetic interactions in ameliorating TE performance and offers inspiration for the development of high‐efficiency TE modules.

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R2) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance.

The location of γ-ray emission of blazars remains a contested topic, inspiring the development of numerous investigative techniques to address this issue. In this work, we analyzed Fermi γ-ray light curves in the GeV and MeV bands, employing the discrete cross-correlation function method to discern time lags between the two bands. For 4C +21.35, Ton 599, B2 1420+32, and PKS 1510-089, we identified a time lag spanning several days, while for PKS 1441+25, the time lag was not statistically found. The results imply that the soft photons necessary for inverse Compton scattering predominantly originate from the dusty torus in the first four sources, whereas for PKS 1441+25, they seem to be sourced mainly from the broad-line region. Further analysis of the opacity (τ γ γ ) and the GeV spectra study supports the conclusion that the location of the dissipation region must be beyond the BLR to avoid significant absorption. Notably, for PKS 1441+25, the emission region is also posited to lie outside yet proximate to the BLR. The parameters of describing the emission region were obtained by fitting broadband spectral energy distribution with contemporaneous observation data. Our findings suggest that for the five TeV FSRQs, during TeV flaring events, the jet appears to maintain an equilibrium between the energy density of the magnetic field and that of the particles for all investigated sources, with the exceptions of 4C +21.35 and PKS 1441+25. In terms of the overall jet power, particle energy is the dominant contributor, and the observed blazar radiation cannot be solely attributed to the magnetic field, except in the case of 4C +21.35. Consequently, magnetic reconnection is unlikely to be the primary mechanism behind particle acceleration in these systems.

In this study we present a novel Monte Carlo code, referred to as GNC, which enables the investigation of dynamical relaxation in clusters comprising multiple mass components in the vicinity of supermassive black holes at the centers of galaxies. Our method is based on two-dimensional Fokker–Planck equations in the energy and angular momentum space, and allows the evolution of multiple mass components, including stars and compact objects. The code demonstrates remarkable flexibility in incorporating additional complex dynamics. By employing a weighting method, we effectively enhance the statistical accuracy of rare particle results. In this initial publication, we present the fundamental version of our method, focusing on two-body relaxations and loss cone effects. Through comparisons with previous studies, we establish consistent outcomes in terms of relaxation processes, energy and angular momentum distributions, density profiles, and loss cone consumption rates. We consistently observe the development of tangential anisotropy within the cluster, while the outer regions tend to retain near-isotropic characteristics. GNC holds great promise for exploring a wide range of intriguing phenomena within galactic nuclei, including relativistic stellar dynamics, providing detailed and insightful outcomes.

The Fermi Large Area Telescope (LAT) has detected 6659 γ-ray sources in the incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi-LAT catalog of γ-ray sources and 3743 of them are blazars, including 1517 blazar candidates of uncertain type (BCUs). Blazars are generally classified by properties of emission lines into BL Lac objects and flat spectrum radio quasars (FSRQs). However, BCUs are difficult to classify because of the lack of spectrum. In this work we apply five different machine-learning algorithms (K-nearest neighbors, logistic regression, support vector machine, random forest, CatBoost) to evaluate the classification of 1517 BCUs based on the observational data of 4FGL-DR3. The results indicate that the use of recursive feature elimination cross-validation can effectively improve the accuracy of models and reduce computation time. We use our models to predict the BCUs from 4FGL-DR3 and the results of the overlapping of the five models are as follows: 811 BL Lac objects, 397 FSRQs, and 309 BCUs.