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

TL;DR

Skykatana is an open source, scalable framework designed to efficiently create and combine sky masks for large astronomical surveys, enabling robust data analysis with limited computational resources.

Contribution

It introduces a novel, resource-efficient pipeline for constructing and combining high-resolution sky masks using hierarchical data structures, tailored for large-scale surveys.

Findings

Successfully applied to Subaru HSC-WISE and Rubin data sets.

Demonstrated efficient mask generation with minimal memory usage.

Provided publicly available code and masks for community use.

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 at https://github.com/samotracio/skykatana, and the LSST masks can be obtained from https://osf.io/r5vw6