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SPHEREx confirms predictions for artificial satellite trail pollution in Low Earth Orbit

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abstract

The number of artificial satellites in Low Earth Orbit (LEO) is increasing at an exponential rate since 2019. Satellites are visible to both ground and space telescopes, and their bright emission in optical, infrared, and radio-wavelengths contaminate astronomical observations, degrading the data's scientific value. Recent simulations forecast that if all satellite constellations listed in current launch manifests are deployed to LEO, satellite trails will appear in up to 96\% of the images obtained by most space telescopes. In this article, we use the recently launched SPHEREx space telescope to corroborate these models. SPHEREx observations obtained between May and September 2025 indicate that $73.3^{+1.3}_{-1.2}\%$ of the images already show satellite trail contamination, with an average number of $N=2.18^{+0.11}_{-0.09}$ trails per exposure, providing observational validation of the published light contamination models. The observed satellite trails display highly inclined trajectories in agreement with the simulated ones. We discuss potential data reduction mitigation methods, and provide an updated satellite light pollution forecast for \emph{Hubble} and SPHEREx including the newer satellite constellations proposed in early 2026.

fields

astro-ph.IM 1

years

2026 1

verdicts

UNVERDICTED 1

representative citing papers

Streak detection in the VST/OmegaCAM archive using deep learning

astro-ph.IM · 2026-06-29 · unverdicted · novelty 4.0

A two-stage deep learning pipeline (HT-LCNN detector + VGG6 classifier) trained on augmented real and simulated data detects streaks in OmegaCAM frames with F1 > 0.95 on test sets and 0.99 precision on real 2023 data, uncovering 25,335 streaks including >20% uncatalogued objects across 1.2 million f

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  • Streak detection in the VST/OmegaCAM archive using deep learning astro-ph.IM · 2026-06-29 · unverdicted · none · ref 38 · internal anchor

    A two-stage deep learning pipeline (HT-LCNN detector + VGG6 classifier) trained on augmented real and simulated data detects streaks in OmegaCAM frames with F1 > 0.95 on test sets and 0.99 precision on real 2023 data, uncovering 25,335 streaks including >20% uncatalogued objects across 1.2 million f