Rui Sun
PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework
Sun, Rui; Jia, Peng; Sun, Yongyang; Yang, Zhimin; Liu, Qiang; Wei, Hongyan
Authors
Peng Jia
Yongyang Sun
Zhimin Yang
Qiang Liu
Hongyan Wei
Abstract
Time-domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the development of an algorithm capable of detecting them and determining their magnitudes and positions has become imperative. Leveraging the advancements in deep neural networks, we present PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions, but also to estimate the photometric uncertainty. PNet comprises two essential steps. First, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, PNet estimates the uncertainty associated with the photometry results, serving as a valuable reference for the light-curve classification algorithm. Our algorithm has been tested using both simulated and real observation data, demonstrating the ability of PNet to deliver consistent and reliable outcomes. Integration of PNet into data-processing pipelines for time-domain astronomy holds significant potential for enhancing response speed and improving the detection capabilities for celestial objects with variable positions and magnitudes.
Citation
Sun, R., Jia, P., Sun, Y., Yang, Z., Liu, Q., & Wei, H. (2023). PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework. Astronomical Journal, 166(6), Article 235. https://doi.org/10.3847/1538-3881/ad01b5
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 9, 2023 |
Online Publication Date | Nov 9, 2023 |
Publication Date | Dec 1, 2023 |
Deposit Date | Nov 15, 2023 |
Publicly Available Date | Nov 15, 2023 |
Journal | The Astronomical Journal |
Print ISSN | 0004-6256 |
Publisher | IOP Publishing |
Peer Reviewed | Peer Reviewed |
Volume | 166 |
Issue | 6 |
Article Number | 235 |
DOI | https://doi.org/10.3847/1538-3881/ad01b5 |
Keywords | Time domain astronomy, CCD photometry, Photographic astrometry, Bayesian statistics, Neural networks |
Public URL | https://durham-repository.worktribe.com/output/1907358 |
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Copyright Statement
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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