HAYATE: Photometric redshift estimation by hybridising machine learning with template fitting
arxiv(2024)
摘要
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 (σ_NMAD) than EAZY in the
initial low-z region (z<1.3), while being comparable even in the high-z
extrapolated regime (1.3更多
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