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We present a new machine learning model for estimating photometric redshiftswith improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending onthe estimation range of redshifts, this model based on neural networks canhandle the difficulty for inferring photometric redshifts. Moreover, to reducebias induced by the new model's ability to deal with estimation difficulty, itexploits the power of ensemble learning. We extensively examine the mappingbetween input features and target redshift spaces to which the model is validlyapplicable to discover the strength and weaknesses of trained model. Becauseour trained model is well calibrated, our model produces reliable confidenceinformation about objects with non-catastrophic estimation. While our model ishighly accurate for most test examples residing in the input space, wheretraining samples are densely populated, its accuracy quickly diminishes forsparse samples and unobserved objects (i.e., unseen samples) in training. Wereport that out-of-distribution (OOD) samples for our model contain bothphysically OOD objects (i.e., stars and quasars) and galaxies with observedproperties not represented by training data. The code for our model isavailable at https://github.com/GooLee0123/MBRNN for other uses of the modeland retraining the model with different data.

Estimation of Photometric Redshifts. I. Machine Learning Inference for Pan-STARRS1 Galaxies Using Neural Networks