Automated SpectroPhotometric Image REDuction (ASPIRED)

TL;DR

ASPIRED is an open-source Python toolkit that automates the reduction of long-slit spectroscopic data, providing quick, science-ready results adaptable to various instruments, facilitating real-time data analysis in astronomy.

Contribution

It introduces a flexible, Python-based spectral data-reduction toolkit that simplifies and automates the process for diverse instruments, reducing reliance on traditional IRAF-based pipelines.

Findings

ASPIRED produces spectra comparable in quality to IRAF and STARLINK pipelines.

The toolkit enables near-real-time data reduction, supporting adaptive observing strategies.

It is suitable for low-resolution long-slit spectrometers with minimal parameter adjustments.

Abstract

We provide a suite of public open-source spectral data-reduction software to rapidly obtain scientific products from all forms of long-slit-like spectroscopic observations. Automated SpectroPhotometric REDuction (ASPIRED) is a Python-based spectral data-reduction toolkit. It is designed to be a general toolkit with high flexibility for users to refine and optimize their data-reduction routines for the individual characteristics of their instruments. The default configuration is suitable for low-resolution long-slit spectrometers and provides a quick-look quality output. However, for repeatable science-ready reduced spectral data, some moderate one-time effort is necessary to modify the configuration. Fine-tuning and additional (pre)processing may be required to extend the reduction to systems with more complex setups. It is important to emphasize that although only a few parameters need updating, ensuring their correctness and suitability for generalization to the instrument can take time due to factors such as instrument stability. We compare some example spectra reduced with ASPIRED to published data processed with iraf-based and STARLINK-based pipelines, and find no loss in the quality of the final product. The Python-based, iraf-free ASPIRED can significantly ease the effort of an astronomer in constructing their own data-reduction workflow, enabling simpler solutions to data-reduction automation. This availability of near-real-time science-ready data will allow adaptive observing strategies, particularly important in, but not limited to, time-domain astronomy.