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Identifying and Measuring Satellite Streaks in DECam Images

Alexandra Serrano Mendoza, Meredith L. Rawls, Andrés Alejandro Plazas Malagón

Abstract

The rapid growth of satellite constellations, particularly Starlink, is increasingly affecting ground-based astronomy. In this project, we developed a workflow to detect, identify, and measure the brightness of trails from artificial satellites and other orbiting objects in archival images from the Dark Energy Camera (DECam), available through the NOIRLab Data Archive. We filtered images with visible streaks, retrieved detector-level images, applied the Hough Transform (via satmetrics) to detect and align trails, and performed surface brightness photometry. We also used SatChecker to obtain likely identifications for each trail. Our sample of nine measured streaks includes Starlink satellites, a navigation satellite, a decommissioned science satellite, and a rocket body. Our results show that satellites and other orbiting objects are consistently detectable in DECam images, but their brightness varies significantly, reflecting design and operational differences across object types and models. While the methodology proved effective, detecting faint streaks was challenging, and short-lived glints remain an even harder problem for future work. This proof-of-concept establishes a foundation for larger statistical studies of satellite impacts on astronomical surveys. The code is available at https://github.com/iausathub/reca-streaks

Identifying and Measuring Satellite Streaks in DECam Images

Abstract

The rapid growth of satellite constellations, particularly Starlink, is increasingly affecting ground-based astronomy. In this project, we developed a workflow to detect, identify, and measure the brightness of trails from artificial satellites and other orbiting objects in archival images from the Dark Energy Camera (DECam), available through the NOIRLab Data Archive. We filtered images with visible streaks, retrieved detector-level images, applied the Hough Transform (via satmetrics) to detect and align trails, and performed surface brightness photometry. We also used SatChecker to obtain likely identifications for each trail. Our sample of nine measured streaks includes Starlink satellites, a navigation satellite, a decommissioned science satellite, and a rocket body. Our results show that satellites and other orbiting objects are consistently detectable in DECam images, but their brightness varies significantly, reflecting design and operational differences across object types and models. While the methodology proved effective, detecting faint streaks was challenging, and short-lived glints remain an even harder problem for future work. This proof-of-concept establishes a foundation for larger statistical studies of satellite impacts on astronomical surveys. The code is available at https://github.com/iausathub/reca-streaks
Paper Structure (15 sections, 18 equations, 3 figures, 3 tables)

This paper contains 15 sections, 18 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Aperture Photometry Method
  4. PSF-Convolved Trail Fitting Method
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Contributions
  9. Validation of Streak Photometry via Simulations
  10. Simulation Setup
  11. Recovered Values
  12. Streak detection and photometry pipeline: wrapper function, usage example, and tuned parameters
  13. Wrapper function for Hough-based line detection
  14. Example usage in the full pipeline
  15. Hand-tuned parameters for reproducibility

Figures (3)

  • Figure 1: Example of a satellite trail in a DECam exposure (left, CCD 5) compared with SatChecker predictions for satellite passes (right) for NAVSTAR-70 in DECam exposure 1134933. The left panel shows the individual CCD image containing the trail; the right panel shows the predicted satellite paths across the full DECam focal plane, with the trail of interest crossing CCD 5. The predicted satellite path overlaps with the observed streak in the image, confirming the identification.
  • Figure 2: Detection and alignment of a satellite trail using the Hough Transform (via satmetrics). Top left: binary thresholded image produced by the line detection algorithm, highlighting candidate linear features above the background. Top right: Canny edge-detected image, which is used by the Hough Transform to identify and measure the orientation of the trail. Bottom: extracted 2D cutout of the trail after rotation to a horizontal orientation, with the median cross-trail intensity profile overlaid. Pixel values are in counts (ADU).
  • Figure 3: Photometric analysis of a satellite trail (NAVSTAR-70, exposure 1134933, CCD 5; see Table \ref{['tab:streak_photometry']}). Top: 1D brightness profile (in counts) across the streak (mean across the long axis), showing a clear signal above the background. The on-streak and off-streak (background) regions are indicated with dashed vertical lines. Bottom: corresponding 2D image with the regions used for signal and background measurement.