Real or Interloper? The Redshift Likelihoods of z>8 Galaxies in the HUDF12

TL;DR

This paper introduces a Markov Chain Monte Carlo method for more reliable redshift estimation of high-z galaxy candidates, revealing many may be low-redshift interlopers rather than truly distant galaxies.

Contribution

It presents a robust MCMC-based approach for redshift estimation, improving over traditional chi^2 minimization methods in identifying genuine high-redshift galaxies.

Findings

None of the 13 candidate galaxies are securely confirmed as high-redshift.

There is an average 21% probability that these sources are low-redshift interlopers.

The MCMC method provides more accurate credible intervals for redshift estimates.

Abstract

In the absence of spectra, fitting template model spectra to observed photometric fluxes, known as Spectral Energy Distribution (SED) fitting, has become the workhorse for identifying high-z galaxies. In this paper, we present an analysis of the most recent and possibly most distant galaxies discovered in the Hubble Ultra Deep Field using a more robust method of redshift estimation based on Markov Chain Monte Carlo fitting (MCMC), rather than relying on the redshift of "best fit" models obtained using common chi^2 minimization techniques. The advantage of MCMC fitting is the ability to accurately estimate the probability density function of the redshift, as well as any other input model parameters, allowing us to derive accurate credible intervals by properly marginalizing over all other input model parameters. We apply our method to 13 recently identified sources and show that, despite claims based on chi^2 minimization, none of these sources can be securely ruled out as low redshift interlopers given the low signal-to-noise of currently available observations. We estimate that there is an average probability of 21% that these sources are low redshift interlopers.