ArXSP: A python-based modular application for the reduction of digitized archival spectra

We present a methodology for the reduction of archival spectral data together with the description of a newly developed Python-based software package featuring an interactive graphical interface. The work is primarily aimed at processing spectra obtained with electron–optical converters (EOCs), which are characterized by geometric distortions induced by the magnetic field of the registration system. Such data are preserved, in particular, in the archive of the Fesenkov Astrophysical Institute (FAI), which contains about 10,000 photographic plates. These distortions, along with the need to transform the optical density of the photographic material into relative intensity, cannot be corrected by standard astronomical packages such as IRAF and therefore require a dedicated approach. Historically, reductions at FAI were performed using a program written in the Microsoft QuickC language for computing platforms of the 1990s, rendering it incompatible with modern operating systems. The new package is implemented with the PyQt5 framework, retaining the logic of the original code while extending its functionality. The implemented algorithms include image rotation and cropping, geometric distortion correction, construction of the characteristic curve linking optical density and intensity, and direct conversion of pixel values in object spectra. The developed software ensures reproducible reduction of archival spectra and provides a cross-platform environment with potential for further extensions.

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Publication Details

Journal
Astronomy and Computing
Published
2025-12-19
DOI
https://doi.org/10.1016/j.ascom.2025.101050
Primary Topic
Astronomical Observations and Instrumentation
Type
article
Field-Weighted Citation Impact
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article

ArXSP: A python-based modular application for the reduction of digitized archival spectra

Saule Shomshekova, Adel Umirbayeva, Ildana Izmailova, Laura Aktay et al.
Astronomy and Computing
Astronomical Observations and Instrumentation
article

ArXSP: A python-based modular application for the reduction of digitized archival spectra

Saule Shomshekova, Adel Umirbayeva, Ildana Izmailova, Laura Aktay, Manas Khassanov
article en

Abstract

We present a methodology for the reduction of archival spectral data together with the description of a newly developed Python-based software package featuring an interactive graphical interface. The work is primarily aimed at processing spectra obtained with electron–optical converters (EOCs), which are characterized by geometric distortions induced by the magnetic field of the registration system. Such data are preserved, in particular, in the archive of the Fesenkov Astrophysical Institute (FAI), which contains about 10,000 photographic plates. These distortions, along with the need to transform the optical density of the photographic material into relative intensity, cannot be corrected by standard astronomical packages such as IRAF and therefore require a dedicated approach. Historically, reductions at FAI were performed using a program written in the Microsoft QuickC language for computing platforms of the 1990s, rendering it incompatible with modern operating systems. The new package is implemented with the PyQt5 framework, retaining the logic of the original code while extending its functionality. The implemented algorithms include image rotation and cropping, geometric distortion correction, construction of the characteristic curve linking optical density and intensity, and direct conversion of pixel values in object spectra. The developed software ensures reproducible reduction of archival spectra and provides a cross-platform environment with potential for further extensions.

Astronomy and ComputingVol. 55
Al-Farabi Kazakh National University (KZ), Fesenkov Astrophysical Institute (KZ)
Openalex Percentile: Top 58%
Astronomical Observations and Instrumentation
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ArXSP: A python-based modular application for the reduction of digitized archival spectra — Saule Shomshekova, Adel Umirbayeva, et al. · Astronomy and Computing (2025) | TGRS Research Map | TGRS