Using 23 Years of ACS/SBC Data to Understand Backgrounds: Explaining & Predicting Background Variations

TL;DR

This study analyzes 23 years of Hubble ACS/SBC data to understand and predict background variations using a machine learning model based on observational parameters, revealing key factors influencing background levels.

Contribution

Developed a machine learning model that accurately predicts SBC background variations from observational parameters, enhancing understanding of background drivers.

Findings

Background varies over an order of magnitude across observations.

Key factors influencing background include Solar elevation and separation angle.

Model accurately predicts background levels based on observational parameters.

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.