Speaker
Description
The volume of galaxy imaging data continues to outpace that of the high quality spectroscopic data used to precisely measure physical parameters like redshift and specific star formation rate (sSFR). In the near future, this problem will be exacerbated as facilities like Rubin and Roman rapidly build up sky maps extending to higher redshifts and dimmer fluxes, regimes where the spectroscopic datasets provide an even more sparse and biased sampling of the galaxy population than among brighter, lower redshift objects. Image properties like photometric colors can be mapped to physical parameters using spectroscopic galaxies as training data, but in lower-dimensional color spaces there tend to be degenerate solutions, while in higher-dimensional spaces the curse of dimensionality exacerbates the deficient sampling by labeled data. However, since observed galaxy colors are highly correlated and driven by a small number of physical parameters (mainly redshift and sSFR), this is a perfect case for dimensionality reduction. Self-organizing maps (SOMs) have become a popular approach among astronomers, but feature drawbacks like information loss to binning data in cells and constrained geometry. To ameliorate these issues, I use uniform manifold approximation and projection (UMAP) to study the color–redshift relation, compressing a seven-dimensional Rubin+Roman-like color space to three dimensions and recovering a thin, densely-sampled manifold with monotonic and roughly orthogonal trends in redshift and sSFR. Position in this compressed color space maps coherently to redshift, such that it can be reliably interpolated from only a small and highly-biased subset of labeled data using a simple kNN-based estimator.