Hayate: photometric redshift estimation by hybridizing machine learning with template fitting

Abstract

ABSTRACT Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template-fitting methods but may not generalize well on new data that deviates from that in the training set. In this work, we present a Hybrid Algorithm for WI(Y)de-range photo-z estimation with Artificial neural networks and TEmplate fitting (hayate), a novel photo-z method that combines template fitting and data-driven approaches and whose training loss is optimized in terms of both redshift point estimates and probability distributions. We produce artificial training data from low-redshift galaxy spectral energy distributions (SEDs) at z < 1.3, artificially redshifted up to z = 5. We test the model on data from the ZFOURGE surveys, demonstrating that hayate can function as a reliable emulator of eazy for the broad redshift range beyond the region of sufficient spectroscopic completeness. The network achieves precise photo-z estimations with smaller errors (σNMAD) than eazy in the initial low-z region (z < 1.3), while being comparable even in the high-z extrapolated regime (1.3 < z < 5). Meanwhile, it provides more robust photo-z estimations than eazy with the lower outlier rate ($\eta _{0.2}\lesssim 1~{{\ \rm per\ cent}}$) but runs ∼100 times faster than the original template-fitting method. We also demonstrate hayate offers more reliable redshift probability density functions, showing a flatter distribution of Probability Integral Transform scores than eazy. The performance is further improved using transfer learning with spec-z samples. We expect that future large surveys will benefit from our novel methodology applicable to observations over a wide redshift range.