  We present zephyr, a novel method that integrates cutting-edge normalizing
flow techniques into a mixture density estimation framework, enabling the
effective use of heterogeneous training data for photometric redshift
inference. Compared to previous methods, zephyr demonstrates enhanced
robustness for both point estimation and distribution reconstruction by
leveraging normalizing flows for density estimation and incorporating careful
uncertainty quantification. Moreover, zephyr offers unique interpretability by
explicitly disentangling contributions from multi-source training data, which
can facilitate future weak lensing analysis by providing an additional quality
assessment. As probabilistic generative deep learning techniques gain
increasing prominence in astronomy, zephyr should become an inspiration for
handling heterogeneous training data while remaining interpretable and robustly
accounting for observational uncertainties.
