Map-making for large-format detector arrays on CCAT

TL;DR

This paper addresses the challenge of creating maps from large data volumes generated by CCAT's large-format detector arrays, proposing a distributed processing extension of the SMURF map-maker to handle increased data rates efficiently.

Contribution

It introduces a method to extend the SMURF map-maker for distributed-node parallel processing, enabling efficient handling of CCAT's large data volumes.

Findings

Distributed SMURF scales with number of nodes

Processing time reduces with more nodes

Feasibility of large-scale map-making demonstrated

Abstract

CCAT is a large submillimetre telescope to be built near the ALMA site in northern Chile. A large-format KID camera, with up to 48,000 detectors at a single waveband sampled at about 1 kHz, will have a data rate about 50 times larger than SCUBA-2, the largest existing submillimetre camera. Creating a map from this volume of data will be a challenge, both in terms of memory and processing time required. We investigate how to extend SMURF, the iterative map-maker used for reducing SCUBA-2 observations, to a distributed-node parallel system, and estimate how the processing time scales with the number of nodes in the system.