Speaker
Description
We developed a novel generated neural network to reconstruct the rest-frame spectra by giving the observed spectra and their flux error. This network provided all the necessary information for modeling spectra, including the eigenspectra and coefficient. Using this reconstruction, we can achieve the classifying, redshift estimation, and anomaly detection in the same framework. Our test demonstrates we reach the same level of accuracy in spectral fitting and redshift estimation as the classical method, with at least O(10^(-3)) faster. Combining our previous work (GaSNet-II), which used sub-network assembly, we can achieve error estimation and subclassify for future spectroscopy survey (for 4MOST) pipelines. Those series of deep-learning tools will be implemented in the 4MOST classification pipeline.
