Cutana: A High-Performance Tool for Astronomical Image Cutout Generation at Petabyte Scale

TL;DR

Cutana is a high-performance, memory-efficient tool designed for large-scale astronomical image cutout generation, significantly outperforming existing solutions and enabling rapid processing of petabyte-scale datasets.

Contribution

We developed Cutana, a novel batch processing tool optimized for large-scale astronomical data, with automated memory management and support for multiple output formats.

Findings

Cutana processes thousands of cutouts per second.

It can process the entire Q1 dataset in under four hours.

Outperforms Astropy's Cutout2D in all tested scenarios.

Abstract

The Euclid Quick Data Release 1 (Q1) encompasses 30 million sources across 63.1 square degrees, marking the beginning of petabyte-scale data delivery through Data Release 1 (DR1) and subsequent releases. Systematic exploitation of such datasets requires extracting millions of source-specific cutouts, yet standard tools like Astropy's Cutout2D process sources individually, creating bottlenecks for large catalogues. We introduce Cutana, a memory-efficient software tool optimised for batch processing in both local and cloud-native environments. Cutana employs vectorised NumPy operations to extract cutout batches simultaneously from FITS tiles, implements automated memory-aware scheduling, and supports both Zarr and FITS output formats with multiple common normalisation schemes (asinh, log, zscale). Cutana outperforms Astropy in all tested Q1 subset scenarios achieving near linear scaling and processing thousands of cutouts per second. On just four worker threads, Cutana can process all of Q1 in under four hours. The tool includes an ipywidget interface for parameter configuration and real-time monitoring. Integration with ESA Datalabs is underway for the Euclid DR1 release, with open-source release pending ESA open-source licensing processes.