A beta-VAE analysis of pop-cosmos models finds that five latent dimensions capture the rest-frame optical SED, corresponding to stellar mass, recent star formation, dust, and two gas ionization states.
ANNz: estimating photometric redshifts using artificial neural networks
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abstract
We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the r.m.s. redshift error in the range 0 < z < 0.7 is 0.023. Non-ideal conditions (spectroscopic sets which are small, or which are brighter than the photometric set for which redshifts are required) are simulated and the impact on the photometric redshift accuracy assessed.
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Galaxy size-mass relations exhibit double power-law breaks at different pivot masses for quiescent versus bulge-dominated samples, coinciding with AGN activity scales.
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
citing papers explorer
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pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches
A beta-VAE analysis of pop-cosmos models finds that five latent dimensions capture the rest-frame optical SED, corresponding to stellar mass, recent star formation, dust, and two gas ionization states.
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pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web
Galaxy size-mass relations exhibit double power-law breaks at different pivot masses for quiescent versus bulge-dominated samples, coinciding with AGN activity scales.
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Machine Learning Techniques for Astrophysics and Cosmology: Photometric Redshifts
AI techniques for photometric redshift estimation have converged and are now limited by the size, systematics, and selection effects in spectroscopic training samples rather than by methodology.
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.