{
  "annotation_id": "6183d1e4-5f8a-4e44-a89c-d34a0c15b1be",
  "date_created": "2026-02-17T05:53:20.450000Z",
  "date_modified": "2026-02-17T05:53:20.450000Z",
  "file_hash": "b7b28f6883fd3d4343674b7c993c5258b5e7313b9462a2e78eefc2b2e7dc65a7",
  "private": false,
  "record": {
    "abstract": "Recent analysis of 23 years of Hubble Space Telescope ACS/SBC data has shown that background levels can vary considerably between observations, with most filters showing over an order of magnitude variation. For the shorter-wavelength filters, the background is understood to be dominated by airglow; however, what precisely drives background variations is not well constrained for any filter. Here, we explore the causes of the background variation. Using over 8,000 archival SBC observations, we developed a machine learning model that can accurately predict the background for an observation, based upon a set of 23 observational parameters. This model indicates that, depending on filter, the SBC background is generally dominated by Solar elevation, Solar separation angle, Earth limb angle of observation, SBC temperature, and target Galactic latitude.",
    "arxiv_id": "2601.09682",
    "authors": [
      "Christopher J. R. Clark",
      "Roberto J. Avila",
      "Alyssa Guzman",
      "Norman A. Grogin"
    ],
    "categories": [
      "astro-ph.IM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Using 23 Years of ACS/SBC Data to Understand Backgrounds:Explaining \u0026 Predicting Background Variations",
    "url": "https://arxiv.org/abs/2601.09682",
    "version": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "b95e9fc1-cacc-44d2-b6e6-f58c37e9224c",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}