The Deep Neural Network based Photometry and Astrometry Framework for
  Wide Field Small Aperture Telescopes
  Wide field small aperture telescopes (WFSATs) are preferable observation
instruments for time domain astronomy, because they could obtain images of
celestial objects with high cadence in a cost-effective way. An automatic data
processing algorithm which could detect celestial objects and obtain their
positions and magnitudes from observed images is important for further
scientific research. In this paper, we extend the ability of a deep neural
network based astronomical target detection algorithm to make it suitable for
photometry and astrometry, by adding two new branches. Because the photometry
and astrometry neural network are data-driven regression algorithms, limited
training data with limited diversity would introduce the epistemic uncertainty
to final regression results. Therefore, we further investigate the epistemic
uncertainty of our algorithm and have found that differences of background
noises and differences of point spread functions between the training data and
the real would introduce uncertainties to final measurements. To reduce this
effect, we propose to use transfer learning strategy to train the neural
network with real data. The algorithm proposed in this paper could obtain
types, positions and magnitudes of celestial objects with high accuracy and
cost around 0.125 second to process an image, regardless of its size. The
algorithm could be integrated into data processing pipelines of WFSATs to
increase their response speed and detection ability to time-domain astronomical
events.
