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[1308.4145] The first analytical expression to estimate pho

Posted: February 06 2014
by brian nord
This work uses a machine-learning software technology to estimate photometric redshifts: more importantly, it estimates the functional form of the relationships between photometry and redshift. The technology performs a search over an increasingly complicated functional space, using a software called EUREQA, which has also been developed into both educationally and commercially licensed software. It appears very mature.

Does a method like sufficiently sample the space of possible relationships between photometry and redshift?

Is there a probabilistic interpretation of their results? I.e., how would one obtain a p-value or significance of the output? Here, the question is regarding assigning objects to classes probabilistically ... e.g., types of galaxies.

How reliable is this work's methodology? Are any major drawbacks not discussed? If not, why are more people not using this method?

[1308.4145] The first analytical expression to estimate phot

Posted: March 21 2014
by Maciej Bilicki
You can easily imagine that the best-fit formula will depend on the training data as well as any other variables such as the passbands/colors to use.

Same happens with any empirical methods: you need to retrain your e.g. neural networks if the properties of your input data change.

After all, there's no one fixed relation between magnitudes and redshifts: galaxies are not all the same.