Predicting Galaxy Interloper Fraction with GNNs

Upcoming emission line spectroscopic surveys, such as Euclid and the Roman Space Telescope, will be prone to systematics due to the presence of interlopers: galaxies whose redshift and distance from us are miscalculated due to line confusion in their emission spectra. Particularly pernicious are interlopers involving the confusion between two lines with close emitted wavelengths, since these interlopers correlate with the target galaxies. An interesting example is H$\beta$ emitters confused as \oiii\ emitters. They introduce a particular pattern in the 3D distribution of the observed galaxy catalog that can bias the cosmological analysis performed with that sample. We present a novel method to predict the fraction of interlopers in a galaxy catalog, using simulations and halos as a proxy for galaxies. This method uses Graph Neural Networks to learn the posterior distribution of the interloper fraction while marginalizing over cosmological and astrophysics unknowns.