The Impact of Satellite Trails on Hubble Space Telescope Observations: satellite classifications

(2022) The Impact of Satellite Trails on Hubble Space Telescope Observations: satellite classifications. Zenodo doi:10.5281/zenodo.7474191 [Dataset]

Abstract

This repository contains the Hubble Space Telescope (HST) observations with satellite classifications, released in the paper "The Impact of Satellite Trails on Hubble Space Telescope Observations" (DOI: 10.1038/s41550-023-01903-3). This table contains 114 607 individual HST images taken in the last 19 years and publicly released in the eHST archive by 3 October 2021, with satellite trail classifications made with machine learning and citizen science. We processed the individual images by adding the two ACS/WFC (WFC3/UVIS, respectively) apertures side-by-side, without correcting for geometric distortions and without the gap between the two detectors (hence why the satellite trails can appear discontinuous in the images). Note that these are not original images from the eHST archive, and therefore are not meant for other scientific analysis. The dataset contains 3072 HST images with satellites (2.7% of the dataset) and 3228 satellite trails in total. The classifications were visually inspected and vetted by the authors (images are flagged with the 'satellite' flag). The table contains the following columns: observation IDs: both individual exposures (simple_id) and composite images, multiple individual exposures processed and stacked (composite_id); instrument: ACS/WFC or WFC3/UVIS; start time and end time of exposure; exposure duration; right ascension (ra) and declination (dec); 'satellite' flag [empty otherwise]; no_sat: number of satellites in the image; image URL - URL for the individual HST observations used for satellite classification; additional metadata columns, as available in the eHST archive. The satellites were classified by volunteers on the Hubble Asteroid Hunter citizen science project and with a machine learning classifier. Please cite the paper (Kruk et al., https://www.nature.com/articles/s41550-023-01903-3) when using the data in this repository.

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Contributors

Contributor: Megan E. Perks

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