A CNN with attention and shared latent space recovers SFHs and metallicities from spectro-photometric data with ~0.12 dex age and ~0.03 dex metallicity dispersion while running thousands of times faster than full spectral fitting.
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Empirical aperture corrections for SDSS absorption indices, derived via CALIFA simulations, reach 15% of dynamical range at low redshift and reduce overestimates of old galaxy fractions by up to 10%.
citing papers explorer
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Determining star formation histories and age-metallicity relations with convolutional neural networks
A CNN with attention and shared latent space recovers SFHs and metallicities from spectro-photometric data with ~0.12 dex age and ~0.03 dex metallicity dispersion while running thousands of times faster than full spectral fitting.
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Correcting the fiber-aperture bias affecting galaxy stellar populations in the Sloan Digital Sky Survey. Aperture corrections to absorption indices based on CALIFA integral field observations
Empirical aperture corrections for SDSS absorption indices, derived via CALIFA simulations, reach 15% of dynamical range at low redshift and reduce overestimates of old galaxy fractions by up to 10%.