Estimating Galaxy Parameters with Self-organizing Maps and the Effect of Missing Data

The current and upcoming large data volume galaxy surveys require the use of machine-learning techniques to maximize their scientific return. This study explores the use of Self-Organizing Maps (SOMs) to estimate galaxy parameters with a focus on handling cases of missing data and providing realistic probability distribution functions for the parameters. We train an SOM with a simulated mass-limited lightcone assuming a ugrizY JHK s +IRAC data set, mimicking the Hyper Suprime-Cam Deep joint data set. For parameter estimation, we derive SOM likelihood surfaces considering photometric errors to derive total (statistical and systematic) uncertainties. We explore the effects of missing data, including which bands are particularly critical to the accuracy of the derived parameters. We demonstrate that the parameter recovery is significantly better when the missing bands are “filled in” rather than if they are completely omitted. We propose a practical method for such recovery of missing data.