arXiv subjects

Zhi-Wei Pan

Publications and source records attributed to Zhi-Wei Pan.

2 recordsLinked to original sources

arXiv2025

Changing-look Active Galactic Nuclei from the Dark Energy Spectroscopic Instrument. V. Dramatic Variability in High-Ionization Broad Emission Lines

We present a systematic search for changing-look (CL) quasars at high redshift z > 0.9 by cross-matching the spectroscopic datasets from the Dark Energy Spectroscopic Instrument Data Release 1 and Sloan Digital Sky Survey Data Release 18. We identify 97 CL quasars showing significant variability in high-ionization broad emission lines, including 45 turn-on and 52 turn-off events, corresponding to a detection rate of approximately 0.042%. This rate is lower than that found for low-ionization CL quasars, likely due to both selection effects and physical differences in high-ionization lines. Based on the CL quasar sample, we find that CL quasars generally exhibit lower accretion rates compared to typical quasars, with average Eddington ratios of log lambda_Edd approximately -1.14 in the bright state and approximately -1.39 in the dim state, compared to approximately -0.65 for typical quasars. While high-ionization lines in CL quasars follow the Baldwin effect on a population level, some individual sources show inverse Baldwin trends. We also find a positive correlation between the variability of high-ionization lines such as Mg II and C III] and the change in bolometric luminosity. In addition, we estimate a characteristic rest-frame timescale of approximately 3 years for CL transitions, with no significant difference between turn-on and turn-off cases. Taken together, these results support an accretion-driven origin for the CL phenomenon and provide new insights into the variability of high-ionization emission lines.

astro-ph.GA↗

arXiv2025

Morpho-Photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog

We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a convolutional neural network branch for learning morphological features from $r$-band images with an artificial neural network branch for extracting spectral energy distribution (SED) information. Specifically, we have used 9-band optical ($ugri$) and NIR ($ZYHJK_s$) data from the Kilo-Degree Survey (KiDS) Data Release 5. The two branches of the network are concatenated and feed into fully-connected layers for final classification. We train the network on a spectroscopically confirmed sample from the Sloan Digital Sky Survey cross-matched with KiDS. The trained model achieves 98.76\% overall accuracy on an independent testing dataset, with F1 scores exceeding 95\% for each class. Raising the output probability threshold, we obtain higher purity at the cost of a lower completeness. We have also validated the network using external catalogs cross-matched with KiDS, correctly classifying 99.74\% of a pure star sample selected from Gaia parallaxes and proper motions, and 99.74\% of an external galaxy sample from the Galaxy and Mass Assembly survey, adjusted for low-redshift contamination. We apply the trained network to 27,335,836 KiDS DR5 sources with $r \leqslant 23$ mag to generate a new classification catalog. This MNN successfully leverages both morphological and SED information to enable efficient and robust classification of stars, quasars, and galaxies in large photometric surveys.

astro-ph.GA↗