Machine learning based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide field: catalogs of spectroscopic and photometric redshifts

TL;DR

This paper develops a machine learning approach using random forests to estimate photometric redshifts for galaxies in the NEPW field, achieving high accuracy and providing a valuable catalog for future astronomical studies.

Contribution

The study introduces a large spectroscopic sample combined with machine learning to accurately estimate photometric redshifts, including a new catalog of 77,755 sources in the NEPW field.

Findings

Photometric redshifts have a dispersion of 0.028 and an outlier fraction of 7.3%.

The random forest model's uncertainty estimates correlate with outlier likelihood.

Various input combinations do not significantly affect prediction accuracy.

Abstract

We perform an MMT/Hectospec redshift survey of the North Ecliptic Pole Wide (NEPW) field covering 5.4 square degrees, and use it to estimate the photometric redshifts for the sources without spectroscopic redshifts. By combining 2572 newly measured redshifts from our survey with existing data from the literature, we create a large sample of 4421 galaxies with spectroscopic redshifts in the NEPW field. Using this sample, we estimate photometric redshifts of 77755 sources in the band-merged catalog of the NEPW field with a random forest model. The estimated photometric redshifts are generally consistent with the spectroscopic redshifts, with a dispersion of 0.028, an outlier fraction of 7.3%, and a bias of -0.01. We find that the standard deviation of the prediction from each decision tree in the random forest model can be used to infer the fraction of catastrophic outliers and the measurement uncertainties. We test various combinations of input observables, including colors and magnitude uncertainties, and find that the details of these various combinations do not change the prediction accuracy much. As a result, we provide a catalog of 77755 sources in the NEPW field, which includes both spectroscopic and photometric redshifts up to z~2. This dataset has significant legacy value for studies in the NEPW region, especially with upcoming space missions such as JWST, Euclid, and SPHEREx.