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 at https://github.com/samotracio/skykatana, and the LSST masks can be obtained from https://osf.io/r5vw6
@article{arxiv.2512.14848,
title = {Skykatana: a scalable framework to construct sky masks for the Vera Rubin Observatory and large astronomical surveys},
author = {Claudio Lopez and Emilio Donoso and Mariano Javier de L. Dominguez Romero},
journal= {arXiv preprint arXiv:2512.14848},
year = {2026}
}
Comments
21 pages, 18 figures, accepted for publication in Astronomy and Computing Journal