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Teareduce: a Python package with utilities for teaching reduction techniques in Astronomy

Nicolás Cardiel, Sergio Pascual, María Chillarón-Víctor, Cristina Cabello, Jesús Gallego, Jaime Zamorano, María Teresa Ceballos

TL;DR

The paper addresses the need for teaching-oriented data reduction tools in astronomy and presents teareduce as a Python-based educational toolkit designed to illustrate core reduction steps through clear code and Jupyter notebooks. It details modular components for image slicing, wavelength calibration, adaptive spline fitting, and both interactive and automatic cosmic-ray removal, emphasizing classroom usability over general-purpose functionality. The contributions include the SliceRegion classes, TeaWaveCalibration, AdaptiveLSQUnivariateSpline, tea-cleanest, and cr2images, along with plans to incorporate additional CR algorithms such as PyCosmic. The work supports master-level instruction at the Universidad Complutense de Madrid by providing openly accessible code and documentation to foster practical understanding of reduction workflows across instruments and data products.

Abstract

The Python package teareduce has been developed to support teaching activities related to the reduction of astronomical data. Specifically, it serves as instructional material for students participating in practical classes on the processing of astronomical images acquired with various instruments and telescopes. These classes are part of the course Experimental Techniques in Astrophysics, which belongs to the Master's Degree in Astrophysics at the Complutense University of Madrid. The code is publicly available on GitHub, accompanied by a documentation page that includes Jupyter notebooks demonstrating the use of its various classes and functions.

Teareduce: a Python package with utilities for teaching reduction techniques in Astronomy

TL;DR

The paper addresses the need for teaching-oriented data reduction tools in astronomy and presents teareduce as a Python-based educational toolkit designed to illustrate core reduction steps through clear code and Jupyter notebooks. It details modular components for image slicing, wavelength calibration, adaptive spline fitting, and both interactive and automatic cosmic-ray removal, emphasizing classroom usability over general-purpose functionality. The contributions include the SliceRegion classes, TeaWaveCalibration, AdaptiveLSQUnivariateSpline, tea-cleanest, and cr2images, along with plans to incorporate additional CR algorithms such as PyCosmic. The work supports master-level instruction at the Universidad Complutense de Madrid by providing openly accessible code and documentation to foster practical understanding of reduction workflows across instruments and data products.

Abstract

The Python package teareduce has been developed to support teaching activities related to the reduction of astronomical data. Specifically, it serves as instructional material for students participating in practical classes on the processing of astronomical images acquired with various instruments and telescopes. These classes are part of the course Experimental Techniques in Astrophysics, which belongs to the Master's Degree in Astrophysics at the Complutense University of Madrid. The code is publicly available on GitHub, accompanied by a documentation page that includes Jupyter notebooks demonstrating the use of its various classes and functions.
Paper Structure (6 sections, 3 figures)

This paper contains 6 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Teaching reduction techniques in the classroom
  3. Auxiliary classes for image slicing
  4. Wavelength calibration
  5. Adaptive spline fit
  6. Cosmic ray removal

Figures (3)

  • Figure 1: Left: the 2.2m Telescope at the Calar Alto Observatory. Center: students listening to explanations from the observatory staff on the use of the CAFOS instrument. Right: students and faculty of the Master's program conducting observations from the control room of the 2.2m Telescope.
  • Figure 2: Different steps carried out by the TeaWaveCalibration class.
  • Figure 3: Example of cosmic ray detection and correction: original image with multiple cosmic-ray hits (left), identification of a specific cosmic ray with pre-flagged pixels marked by red x's allowing manual selection/deselection (center), and corrected image after interpolation with interpolated pixels marked by x's and pixels used for interpolation indicated by magenta dots (right).