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AI Techniques for Uncovering Resolved Planetary Nebula Candidates from the VPHAS+ Survey

Published online by Cambridge University Press:  06 October 2025

Yushan Li ,
Quentin A. Parker Open the ORCID record for Quentin A. Parker [Opens in a new window] ,
Peng Jia ,
Ruiqi Sun ,
Jaixin Li ,
Xu Li  and
Liang Cao
Yushan Li*
Affiliation:
Laboratory for space research, University of Hong Kong, HK SAR PRC
Quentin A. Parker
Affiliation:
Laboratory for space research, University of Hong Kong, HK SAR PRC
Peng Jia
Affiliation:
Taiyuan University, PRC
Ruiqi Sun
Affiliation:
Taiyuan University, PRC
Jaixin Li
Affiliation:
Taiyuan University, PRC
Xu Li
Affiliation:
Taiyuan University, PRC
Liang Cao
Affiliation:
Taiyuan University, PRC
*
email: quentinp@hku.hk
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Abstract

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AI and deep learning techniques are beginning to play an increasing role in astronomy as a necessary tool to deal with the data avalanche. We describe an application for finding resolved Planetary Nebulae (PNe) in crowded, wide-field, narrow-band Hα survey imagery in the Galactic plane.

Keywords

machine learningartificial intelligenceplanetary nebulaewide-field surveys

Information

Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S384: Planetary Nebulae: A Universal Toolbox in the Era of Precision Astrophysics , December 2023 , pp. 397 - 399
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

References

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