2024/02/01 by Shingo Tanigawa, Karl Glazebrook, Tanigawa, Shingo +7
Environmental Science · Physics and Astronomy · #Color Science and Applications #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Impact of Light on Environment and Health #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Remote Sensing in Agriculture
paper · pdf · doi:10.48550/arxiv.2402.00323
openalex publication_date 2024/02/01 · openalex created_date 2024/02/03 · openalex updated_date 2026/07/28
Machine learning photo-z methods, trained directly on spectroscopic redshifts, provide a viable alternative to traditional template fitting methods but may not generalise 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 optimised in terms of both redshift point estimates and probability distributions. We produce artificial training data from low-redshift galaxy 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