Skykatana: A scalable framework to construct sky masks for the Vera Rubin Observatory and large astronomical surveys

Abstract

Modern wide-field surveys require robust spatial masks to excise bright-star halos, bleed trails, poor-quality regions, and user-defined geometry at scale. We present Skykatana, an open source pipeline that builds and combines boolean HEALPix/HEALSparse maps into science-ready masks and engineered for low-memory operation. Skykatana can efficiently construct, visualize multi-order coverage maps and generate random points in high-resolution masks over half of the celestial sphere with very limited resources and leveraging the hierarchical partition of data the HATS/LSDB framework. We demonstrate two end-to-end applications: (1) a Subaru HSC-WISE composite mask; and (2) Rubin star masks generated on demand in the Rubin Science Platform by querying HATS/LSDB Gaia data and assigning radii from empirical fits to Rubin DP1 data. We release full bright-star masks for various regions of the Rubin footprint and describe performance and scaling. The code, documentation, and examples are publicly available here, and the LSST masks can be obtained in this repository