Kavli Affiliate: David N. Spergel
| First 5 Authors: Christian K. Jespersen, Peter Melchior, David N. Spergel, Andy D. Goulding, ChangHoon Hahn
| Summary:
Galaxies are often modelled as composites of separable components with
distinct spectral signatures, implying that different wavelength ranges are
only weakly correlated. They are not. We present a data-driven model which
exploits subtle correlations between physical processes to accurately predict
infrared (IR) WISE photometry from a neural summary of optical SDSS spectra.
The model achieves accuracies of $chi^2_N approx 1$ for all photometric bands
in WISE, as well as good colors. We are also able to tightly constrain
typically IR-derived properties, e.g. the bolometric luminosities of AGN and
dust parameters such as $mathrm{q_{PAH}}$. We find that current SED-fitting
methods are incapable of making comparable predictions, and that model
misspecification often leads to correlated biases in star-formation rates and
AGN luminosities. To help improve SED models, we determine what features of the
optical spectrum are responsible for our improved predictions, and identify
several lines (CaII, SrII, FeI, [OII] and H$alpha$), which point to the
complex chronology of star formation and chemical enrichment being incorrectly
modelled.
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