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The optical and infrared are connected

Christian K. Jespersen, Peter Melchior, David N. Spergel, Andy D. Goulding, ChangHoon Hahn, Kartheik G. Iyer

TL;DR

This study demonstrates a strong optical–IR coupling in galaxies by predicting infrared WISE photometry from optical SDSS spectra using a data-driven latent-space approach, achieving $χ^2_N\approx1$ across all IR bands and revealing information about AGN, PAHs, and metallic lines that standard SED codes miss. The authors compare a simple empirical mapping based on spender-encoded spectra to CIGALE and prospector, finding the data-driven method far more accurate for joint optical–IR predictions and less prone to overconfident biases. They show that priors alone do not drive the results, while aperture effects and line feature attributions reveal where SED models fail to capture cross-wavelength physics. The work argues that galaxy properties lie on a low-dimensional manifold linking optical and IR emission, suggesting concrete avenues to improve SED models by incorporating spectral line information and latent-space priors, with practical implications for robust AGN and PAH inferences.

Abstract

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 $χ^2_N \approx 1$ 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$α$), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled.

The optical and infrared are connected

TL;DR

This study demonstrates a strong optical–IR coupling in galaxies by predicting infrared WISE photometry from optical SDSS spectra using a data-driven latent-space approach, achieving across all IR bands and revealing information about AGN, PAHs, and metallic lines that standard SED codes miss. The authors compare a simple empirical mapping based on spender-encoded spectra to CIGALE and prospector, finding the data-driven method far more accurate for joint optical–IR predictions and less prone to overconfident biases. They show that priors alone do not drive the results, while aperture effects and line feature attributions reveal where SED models fail to capture cross-wavelength physics. The work argues that galaxy properties lie on a low-dimensional manifold linking optical and IR emission, suggesting concrete avenues to improve SED models by incorporating spectral line information and latent-space priors, with practical implications for robust AGN and PAH inferences.

Abstract

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 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 . 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), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled.

Paper Structure

This paper contains 36 sections, 4 equations, 23 figures, 6 tables.

Figures (23)

  • Figure 1: The redshift distribution of the objects used in this work.
  • Figure 2: A demonstration of predicting WISE photometry (red markers) from the SDSS spectrum (black), shown in observed wavelengths. Although they fit the optical spectrum perfectly, the predictions from the SED models (left panel) have typical deviations of $\sim 10 \sigma$, whereas our empirical approach (right panel) produces typical deviations of $\sim 1 \sigma$. That the photometry can empirically be predicted to $\sim 1 \sigma$ implies that IR photometry is fully determined by the optical spectrum. The multiple lines for prospector are the results of 100 draws from the posterior. The model errors of CIGALE are usually smaller than the points themselves.
  • Figure 3: PP-plot showing the cumulative probability that a sample from the posterior from the empirical model, prospector, or CIGALE will be consistent with the data. A summary of the mean probabilities can be found in Table \ref{['tab:mean_p_fit']}. This metric is generally insensitive to catastrophic errors. Additional precision figures are shown in Appendix \ref{['appsec:more_figures_chi2']}.
  • Figure 4: Color-color as measured from WISE (left) and the predictions of our empirical model (right). All data shown are from the test set. The empirical mapping closely reproduces the observed color-color distributions. Extreme values, especially in the bands with large errors (W3 and W4), are disfavoured by the model because they are likely to be produced by measurement error. However, very high values of W1-W2 are also not perfectly represented, likely due to the limited representation of AGN in our training sample.
  • Figure 5: prospector priors (grey) and posteriors (blue) for a randomly selected galaxy. The posteriors occupy only a very small part of the prior space. Note that the posterior for $\mathrm{f_{AGN}}$ is barely visible, and concentrated in the bottom right corner. There is no AGN in this galaxy.
  • ...and 18 more figures