  Entering the era of large-scale galaxy surveys which will deliver
unprecedented amounts of photometric and spectroscopic data, there is a growing
need for more efficient, data driven, and less model-dependent techniques to
analyze spectral energy distribution of galaxies. In this work, we demonstrate
that by taking advantage of manifold learning approaches, we can estimate
spectroscopic features of large samples of galaxies from their broadband
photometry when spectroscopy is available only for a fraction of the sample.
This will be done by applying the Self Organizing Map (SOM) algorithm on
broadband colors of galaxies and mapping partially available spectroscopic
information into the trained maps. In this pilot study, we focus on estimating
4000A break in a magnitude-limited sample of galaxies in the COSMOS field. We
use observed galaxy colors (ugrizYJH) as well as spectroscopic measurements for
a fraction of the sample from LEGA-C and zCOSMOS spectroscopic surveys to
estimate this feature for our parent photometric sample. We recover the D4000
feature for galaxies which only have broadband colors with uncertainties about
twice of the uncertainty of the employed spectroscopic surveys. Using these
measurements we observe a positive correlation between D4000 and stellar mass
of the galaxies in our sample with weaker D4000 features for higher redshift
galaxies at fixed stellar masses. These can be explained with downsizing
scenario for the formation of galaxies and the decrease in their specific star
formation rate as well as the aging of their stellar populations over this time
period.
