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We present zephyr, a novel method that integrates cutting-edge normalizingflow techniques into a mixture density estimation framework, enabling theeffective use of heterogeneous training data for photometric redshiftinference. Compared to previous methods, zephyr demonstrates enhancedrobustness for both point estimation and distribution reconstruction byleveraging normalizing flows for density estimation and incorporating carefuluncertainty quantification. Moreover, zephyr offers unique interpretability byexplicitly disentangling contributions from multi-source training data, whichcan facilitate future weak lensing analysis by providing an additional qualityassessment. As probabilistic generative deep learning techniques gainincreasing prominence in astronomy, zephyr should become an inspiration forhandling heterogeneous training data while remaining interpretable and robustlyaccounting for observational uncertainties.

Zephyr : Stitching Heterogeneous Training Data with Normalizing Flows for Photometric Redshift Inference