Astronomical Images Quality Assessment with Automated Machine Learning

Olivier Parisot, Pierrick Bruneau, Patrik Hitzelberger

2023

Abstract

Electronically Assisted Astronomy consists in capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation. This practice generates a large quantity of data, which may then be enhanced with dedicated image editing software after observation sessions. In this study, we show how Image Quality Assessment can be useful for automatically rating astronomical images, and we also develop a dedicated model by using Automated Machine Learning.

Paper Citation

in Harvard Style

Parisot O., Bruneau P. and Hitzelberger P. (2023). Astronomical Images Quality Assessment with Automated Machine Learning. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 279-286. DOI: 10.5220/0012073800003541

in Bibtex Style

@conference{data23,
author={Olivier Parisot and Pierrick Bruneau and Patrik Hitzelberger},
title={Astronomical Images Quality Assessment with Automated Machine Learning},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012073800003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Astronomical Images Quality Assessment with Automated Machine Learning
SN - 978-989-758-664-4
AU - Parisot O.
AU - Bruneau P.
AU - Hitzelberger P.
PY - 2023
SP - 279
EP - 286
DO - 10.5220/0012073800003541
PB - SciTePress