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Troy Raen

Publications and source records attributed to Troy Raen.

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VarWISE: Infrared Variability via NEOWISE Single Exposure Photometry

The Near-Earth Object Wide-field Infrared Explorer (NEOWISE) mission provides a decade of all-sky time-series data at 3.4 and 4.6um and an unprecedented opportunity for the discovery and characterization of variable objects. This paper presents VarWISE, a catalog of infrared-variable objects discovered within the NEOWISE single-exposure data. We employ unique methodologies, including the spatial clustering of apparitions and the adoption of novel machine learning-based variable detection (VARnet) and classification (XGBoost) to identify and characterize significant variability. The catalog includes a prediction of variable object type and best-fit period values for each object, if its variations are cyclical, along with other calculated parameters to characterize the nature of the variability. The VarWISE Pure Catalog, containing only variables of highest confidence, has 457,080 objects, 49.81% of which are new discoveries; the VarWISE Extended Catalog, containing all sources, has 1,918,082 objects, 82.02% of which are new. We discuss caveats for each variable type and highlight a few new objects found during a quick perusal of the catalogs' contents.

astro-ph.SR

Reducing the Dimensions of AGN Lightcurve Manifolds

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.

astro-ph.GA