Estimation of Photometric Redshifts. I. Machine Learning Inference for
  Pan-STARRS1 Galaxies Using Neural Networks
  We present a new machine learning model for estimating photometric redshifts
with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on
the estimation range of redshifts, this model based on neural networks can
handle the difficulty for inferring photometric redshifts. Moreover, to reduce
bias induced by the new model's ability to deal with estimation difficulty, it
exploits the power of ensemble learning. We extensively examine the mapping
between input features and target redshift spaces to which the model is validly
applicable to discover the strength and weaknesses of trained model. Because
our trained model is well calibrated, our model produces reliable confidence
information about objects with non-catastrophic estimation. While our model is
highly accurate for most test examples residing in the input space, where
training samples are densely populated, its accuracy quickly diminishes for
sparse samples and unobserved objects (i.e., unseen samples) in training. We
report that out-of-distribution (OOD) samples for our model contain both
physically OOD objects (i.e., stars and quasars) and galaxies with observed
properties not represented by training data. The code for our model is
available at https://github.com/GooLee0123/MBRNN for other uses of the model
and retraining the model with different data.
