Estimation of Photometric Redshifts. II. Identification of
  Out-of-Distribution Data with Neural Networks
  In this study, we propose a three-stage training approach of neural networks
for both photometric redshift estimation of galaxies and detection of
out-of-distribution (OOD) objects. Our approach comprises supervised and
unsupervised learning, which enables using unlabeled (UL) data for OOD
detection in training the networks. Employing the UL data, which is the dataset
most similar to the real-world data, ensures a reliable usage of the trained
model in practice. We quantitatively assess the model performance of
photometric redshift estimation and OOD detection using in-distribution (ID)
galaxies and labeled OOD (LOOD) samples such as stars and quasars. Our model
successfully produces photometric redshifts matched with spectroscopic
redshifts for the ID samples and identifies well the LOOD objects with more
than 98% accuracy. Although quantitative assessment with the UL samples is
impracticable due to the lack of labels and spectroscopic redshifts, we also
find that our model successfully estimates reasonable photometric redshifts for
ID-like UL samples and filter OOD-like UL objects. The code for the model
implementation is available at https://github.com/GooLee0123/MBRNN_OOD.
