# PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework

arXiv: https://arxiv.org/abs/2106.14349 · HTML: https://mlsanity.com/paper/2106.14349/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 71 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data](https://mlsanity.com/paper/1906.11248/) (2019)
- [Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection](https://mlsanity.com/paper/1701.00458/) (2017)
- [Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes](https://mlsanity.com/paper/2002.09211/) (2020)
- [Classifying Image Sequences of Astronomical Transients with Deep Neural Networks](https://mlsanity.com/paper/2004.13877/) (2020)
- [Optical Transient Object Classification in Wide Field Small Aperture Telescopes with Neural Networks](https://mlsanity.com/paper/1904.12987/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes](https://mlsanity.com/paper/2403.10206/) (2024)
- [An Image Quality Evaluation and Masking Algorithm Based On Pre-trained Deep Neural Networks](https://mlsanity.com/paper/2405.03408/) (2024)
- [Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network](https://mlsanity.com/paper/2504.07777/) (2025)

## Abstract (arXiv)

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 the PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions but also to estimate photometry uncertainty. The PNet comprises two essential steps. Firstly, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, the 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 PNet's ability to deliver consistent and reliable outcomes. Integration of the 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.