VarWISE: Infrared Variability via NEOWISE Single Exposure Photometry

TL;DR

VarWISE is a new catalog of infrared-variable objects discovered in NEOWISE data, utilizing machine learning techniques to identify and classify variability, including many new objects.

Contribution

The paper introduces VarWISE, a novel catalog of infrared variables using NEOWISE data and machine learning for detection and classification, with a large number of new discoveries.

Findings

The catalog contains 457,080 high-confidence variables, nearly half are new.

The extended catalog includes 1,918,082 sources, over 82% are new.

Machine learning methods effectively identify and classify variable objects.

Abstract

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.