works on

From the 1 of 14 linked papers with an AI index.

activity
20242026
collaborators

14 papers

astro-ph.HE2026

Radio and X-ray flux rebrightening six years after outburst in a partially-obscured extreme changing-look AGN

Tianyao Zhou, Xinwen Shu, Lei Yang +14

The paper reports multi‑year observations of the partially obscured AGN SDSS J1548+2208, revealing sustained high mid‑infrared and hard X‑ray fluxes and a late‑time radio rebrighte…

astro-ph.GA2026

The Most Luminous H Reverberation Mapping of E1821+643 Indicates the Lower Boundary of the Radius-Luminosity Relation

Sha-Sha Li, Hai-Cheng Feng, Jiancheng Wu +5

The radius-luminosity (-) relation is fundamental to active galactic nucleus (AGN) studies, enabling supermassive black hole (SMBH) mass estimates and AGN-ba…

astro-ph.GA2025

Discovery of a Luminosity-dependent Continuum Lag in NGC 4151 from Photometric and Spectroscopic Continuum Reverberation Mapping

Hai-Cheng Feng, Sha-Sha Li, Mouyuan Sun +17

Accretion onto supermassive black holes (SMBHs) powers active galactic nuclei (AGNs) and drives feedback that shapes galaxy evolution. Constraining AGN accretion disk structure is…

astro-ph.GA2025

Supermassive Black Hole and Broad-line Region in NGC 5548: 2023 Reverberation Mapping Results

Wen-Zhe Xi, Kai-Xing Lu, Jin-Ming Bai +6

We present the results of the 2023 spectroscopic reverberation mapping (RM) campaign for active galactic nuclei (AGN) of NGC 5548, continuing our long-term monitoring program. Usin…

astro-ph.GA2025

Accretion-Regulated Type Transitions in Changing-Look AGNs: Evidence from Two-Epoch Spectral Analysis

Yu-Heng Shen, Kai-Xing Lu, Wei-Jian Guo +6

The changing-look active galactic nucleus (CL-AGN), an extraordinary subpopulation of supermassive black holes, has attracted growing attention for understanding its nature. We pre…

astro-ph.GA2025

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

Hai-Cheng Feng, Rui Li, Nicola R. Napolitano +14

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 i…