分类： 天文学 >> 天文学 提交时间： 2025-06-13 合作期刊: 《Research in Astronomy and Astrophysics》
摘要： This is the second paper in a series that utilizes integral field spectroscopy from MaNGA, NUV imaging from Swift/UVOT, and NIR imaging from 2MASS to investigate dust attenuation properties on kpc scales in nearby galaxies. We apply the method developed in our previous work to the updated SwiM_v4.2 catalog, and measure the optical attenuation curve and the attenuation in three NUV bands for 2487 spaxels selected from 91 galaxies with S/N> 20 and AV > 0.25. We classify all spaxels into two subsets: star-forming (SF) regions and non-SF regions. We explore the correlations of optical opacity (AV) and the optical and NUV slopes of the attenuation curves (AB/AV and Aw2/Aw1) with a broad range of stellar population and emission-line properties, including specific surface brightness of Hα emission (ΣHα/Σ*), stellar age, stellar and gas-phase metallicity, and diagnostics of recent star formation history. Overall, when comparing SF and non-SF regions, we find that AV and AB/AV exhibit similar correlations with all the stellar population and emission-line properties considered, while the NUV slopes in SF regions tend to be flatter than those in non-SF regions. The NUV slope Aw2/Aw1 exhibits an anti-correlation with ΣHα/Σ*, a trend that is primarily driven by the positive correlation between Aw2/Aw1 and Σ*. The NUV slope flattens in SF regions that contain young stellar populations and have experienced recent star formation, but it shows no obvious dependence on stellar or gas-phase metallicity. The spatially resolved dust attenuation properties exhibit no clear correlations with the inclination of host galaxies or the galactocentric distance of the regions. This finding reinforces the conclusion from Paper I that dust attenuation is primarily regulated by local processes on kpc scales or smaller, rather than by global processes at galactic scales.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： A modified physics-informed neural network is used to predict the dynamics of optical pulses including one-soliton, two-soliton, and rogue wave based on the coupled nonlinear Schr\"odinger equation in birefringent fibers. At the same time, the elastic collision process of the mixed bright-dark soliton is predicted. Compared the predicted results with the exact solution, the modified physics-informed neural network method is proven to be effective to solve the coupled nonlinear Schr\"odinger equation. Moreover, the dispersion coefficients and nonlinearity coefficients of the coupled nonlinear Schrodinger equation can be learned by modified physics-informed neural network. This provides a reference for us to use deep learning methods to study the dynamic characteristics of solitons in optical fibers.
分类： 天文学 >> 天文学 提交时间： 2024-03-29 合作期刊: 《Research in Astronomy and Astrophysics》
摘要： Ground-based arrays of imaging atmospheric Cherenkov telescopes (IACTs) are the most sensitive γ-ray detectors for energies of approximately 100 GeV and above. One such IACT is the High Altitude Detection of Astronomical Radiation (HADAR) experiment, which uses a large aperture refractive water lens system to capture atmospheric Cherenkov photons (i.e., the imaging atmospheric Cherenkov technique). The telescope array has a low threshold energy and large field of view, and can continuously scan the area of the sky being observed, which is conducive to monitoring and promptly responding to transient phenomena. The process of γ-hadron separation is essential in very-high-energy (>30 GeV) γ-ray astronomy and is a key factor for the successful utilization of IACTs. In this study, Monte Carlo simulations were carried out to model the response of cosmic rays within the HADAR detectors. By analyzing the Hillas parameters and the distance between the event core and the telescope, the distinction between air showers initiated by γ-rays and those initiated by cosmic rays was determined. Additionally, a Quality Factor was introduced to assess the telescope's ability to suppress the background and to provide a more effective characterization of its performance.
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