A crude but efficient pipeline for JWST MIRI imager : the case of sn1987A

TL;DR

This paper demonstrates that simple, high-level language-based pipelines can effectively process JWST MIRI imager data, offering a low-cost alternative to complex official tools, exemplified through the case of SN1987A.

Contribution

It introduces a minimalist, efficient data processing pipeline for JWST MIRI imager data, emphasizing flexibility and low development cost compared to traditional large-scale infrastructures.

Findings

Simple code can produce scientifically validated results

High-level language pipelines are effective for space data processing

Low-cost, adaptable pipelines benefit small research teams

Abstract

Most of the space projects or large observatories do have official tools like simulators, end-to-end pipelines developed during years by a large team of contributors. They are like {\em cathedrals}. In this paper, we show that very simplistic code using basic operators provided by high level language like GDL allows to write quickly high quality code to process raw data into scientifically validated outputs. This is {\em bazaar}. In this paper we argument why we consider large infrastructure should be designed to allow small ones to benefit from it and allow to graft better alternative processing at very low cost.