TY - JOUR
T1 - Automated SpectroPhotometric Image REDuction (ASPIRED
AU - Lam, Marco C.
AU - Smith, Robert J.
AU - Arcavi, Iair
AU - Steele, Iain A.
AU - Veitch-Michaelis, Josh
AU - Wyrzykowski, Lukasz
N1 - Funding Information:
I.A. is a CIFAR Azrieli Global Scholar in the Gravity and the Extreme Universe Program and acknowledges support from that program, from the ERC under the European Union’s Horizon 2020 research and innovation program (grant agreement number 852097), from the Israel Science Foundation (grant No. 2752/19), from the United States–Israel Binational Science Foundation (BSF), and from the Israeli Council for Higher Education Alon Fellowship.
Funding Information:
This work was partially supported by the Polish NCN grant Daina No. 2017/27/L/ST9/03221.
Funding Information:
Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the Science and Technology Facilities Council (United Kingdom), the National Research Council (Canada), CONICYT (Chile), the Australian Research Council (Australia), Ministério da Ciência e Tecnologia (Brazil), and SECYT (Argentina).
Funding Information:
This work was partially supported by OPTICON. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 730890. This material reflects only the authors’ views and the Commission is not liable for any use that may be made of the information contained therein.
Funding Information:
The LT is operated on the island of La Palma by Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias with financial support from the UK Science and Technology Facilities Council.
Funding Information:
M.C.L. is supported by a European Research Council (ERC) grant under the European Union’s Horizon 2020 research and innovation program (grant agreement number 852097).
Publisher Copyright:
© 2023. The Author(s). Published by the American Astronomical Society.
PY - 2023/6/13
Y1 - 2023/6/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85163843332&partnerID=8YFLogxK
U2 - 10.3847/1538-3881/acd75c
DO - 10.3847/1538-3881/acd75c
M3 - Article
AN - SCOPUS:85163843332
SN - 0004-6256
VL - 166
SP - 1
EP - 16
JO - Astronomical Journal
JF - Astronomical Journal
IS - 1
M1 - 13
ER -