a new kind of photometric redshift

Boris Leistedt came by for an hour to discuss his progress on huge-survey photometric redshift determination. His method is truly novel: It is not a template-based method in the traditional sense, but it is also not a machine-learning method of the train-and-test variety (supervised regression, like Random Forest or equivalent): It uses the causal and noise model of a template method, but fits for the details of every one of the set of templates and also the luminosity functions for the galaxies generated by each template. This makes it possible to build a photometric redshift system entirely from the data but that can be trained with a very small amount of spectroscopy. But most importantly, the model can be trained with with a training set that does not have the same flux or redshift distribution as the bulk of the sources. That is, his method is perfectly matched to the future of imaging surveys. We planned the first paper.

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