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.
Wadadekar,Estimating Photometric Redshifts Using Support Vector Machines,The Publications of the Astronomical Society of the Pacific117(2005) 79 [astro-ph/0412005]
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abstract
We present a new approach to obtaining photometric redshifts using a kernel learning technique called Support Vector Machines (SVMs). Unlike traditional spectral energy distribution fitting, this technique requires a large and representative training set. When one is available, however, it is likely to produce results that are comparable to the best obtained using template fitting and artificial neural networks. Additional photometric parameters such as morphology, size and surface brightness can be easily incorporated. The technique is demonstrated using samples of galaxies from the Sloan Digital Sky Survey Data Release 2 and the hybrid galaxy formation code GalICS. The RMS error in redshift estimation is $<0.03$ for both samples. The strengths and limitations of the technique are 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.
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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.