Reducing the Dimensions of AGN Lightcurve Manifolds
IPAC, California Institute of Technology（加州理工学院 IPAC）；Jet Propulsion Laboratory（喷气推进实验室）；Universidad Rey Juan Carlos（雷乌斯·胡安·卡洛斯国王大学）；Department of Physics and Astronomy, University of California Riverside（加州大学河滨分校物理与天文学系）；Yale University（耶鲁大学）
Shoubaneh Hemmati, Jessica Krick, Daniel Stern, Vandana Desai, Andreas Faisst, Lucas Martin-Garcia, Varoujan Gorjian, Aryana Haghjoo, Farnik Nikakhtar, Troy Rae… 展开作者
Shoubaneh Hemmati, Jessica Krick, Daniel Stern, Vandana Desai, Andreas Faisst, Lucas Martin-Garcia, Varoujan Gorjian, Aryana Haghjoo, Farnik Nikakhtar, Troy Raen, Sogol Sanjaripour, Brigitta M Sipocz, David Shupe
The Active Galactic Nuclei (AGN) glossary is vast and complex. Depending on selection method, observing wavelength, and brightness, AGNs are assigned distinct labels, yet the relationship between different selection methods and the diversity of time-domain behavior within and across classes remains difficult to characterize in a unified framework. Changing-look AGNs (CLAGNs), which transition between classifications over time, further complicate this picture. In this work, we learn a data-driven, low-dimensional representation of multi-wavelength photometric light curves of AGNs, in which the structure of the projected manifold correlates with AGN class and independent spectroscopic properties. Using the NASA Fornax Science Platform, we assemble light curves from ZTF, Pan-STARRS, Gaia, and WISE/NEOWISE for two samples: (1) a heterogeneous set of $\sim$2000 AGNs spanning $z \lesssim 1$, including SDSS quasars, variability-selected sources, and CLAGNs; and (2) a homogeneous sample of $\sim$65000 narrow-line AGNs at $z \approx 0.1$ with well-characterized optical emission-line measurements. Without using class labels during training, the learned manifolds organize variability-selected AGNs into coherent regions of the low-dimensional space, distinguish between turn-on and turn-off CLAGNs, and place tidal disruption events in distinct regions. Manifold coordinates correlate with key spectroscopic and host-galaxy properties, including stellar mass, [OIII] luminosity, and D$_n$(4000), demonstrating that heterogeneous multi-band variability can be combined in a purely data-driven manner to recover correlations with independent physical diagnostics, without requiring explicit physical modeling. These results show that manifold learning offers a practical, assumption-light approach for integrating time-domain surveys and prioritizing spectroscopic follow-up.
Unlocking AGN Variability with Custom ZTF Photometry for High-Fidelity Light Curves and Robust Selection
Universidad de Valparaíso（瓦尔帕莱索大学）；Millennium Nucleus on Transversal Research and Technology to Explore Supermassive Black Holes (TITANS)（探索超大质量黑洞跨学科研究与技术千年核）；Universidad de Chile（智利大学）；Millennium Institute of Astrophysics (MAS)（天体物理千年研究所）；European Southern Observatory（欧洲南方天文台）；Universidad de Tarapacá（塔拉帕卡大学）
Untangling the Complex Nature of AGN Variability with Fairall 9
Durham University（杜伦大学）；Wayne State University（韦恩州立大学）；Institute of Astrophysics, FORTH（希腊基础与研究基金会天体物理研究所）；Eureka Scientific Inc.（尤里卡科学公司）；MIT Kavli Institute for Astrophysics and Space Research（麻省理工学院卡弗里天体物理学与空间研究研究所）；Spectral Sciences Inc.（光谱科学公司）
本研究利用 Fairall 9 的 3 年多波段监测数据，通过傅里叶分辨分析发现 AGN 的 UV 与 X 射线光变性不相关，揭示了其光变性与 SED 变化对理解 AGN 结构与物理的重要意义。
Spectral Handling and Estimation of AGN Parameters (SHEAP), The first AGN fitting GPU-based code
Instituto de Física y Astronomía, Facultad de Ciencias, Universidad de Valparaíso（瓦尔帕莱索大学理学院天体物理与天文研究所）；European Southern Observatory（欧洲南方天文台）；Departamento de Astronomía, Universidad de Chile（智利大学天文学系）；Millennium Nucleus on Transversal Research and Technology to Explore Supermassive Black Holes (TITANS)（探索超大质量黑洞跨学科研究与技术千年核心（TITANS））
