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The construction of catalogues of a particular type of galaxy can be complicated by interlopers contaminating the sample. In spectroscopic galaxy surveys this can be due to the misclassification of an emission line; for example in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) low redshift [OII] emitters may make up a few percent of the observed Ly${alpha}$ emitter (LAE) sample. The presence of contaminants affects the measured correlation functions and power spectra. Previous attempts to deal with this using the cross-correlation function have assumed sources at a fixed redshift, or not modelled evolution within the adopted redshift bins. However, in spectroscopic surveys like HETDEX, where the contamination fraction is likely to be redshift dependent, the observed clustering of misclassified sources will appear to evolve strongly due to projection effects, even if their true clustering does not. We present a practical method for accounting for the presence of contaminants with redshift-dependent contamination fractions and projected clustering. We show using mock catalogues that our method, unlike existing approaches, yields unbiased clustering measurements from the upcoming HETDEX survey in scenarios with redshift-dependent contamination fractions within the redshift bins used. We show our method returns auto-correlation functions with systematic biases much smaller than the statistical noise for samples with at least as high as 7 per cent contamination. We also present and test a method for fitting for the redshift-dependent interloper fraction using the LAE-[OII] galaxy cross-correlation function, which gives less biased results than assuming a single interloper fraction for the whole sample.
Line intensity mapping experiments seek to trace large scale structure by measuring the spatial fluctuations in the combined emission, in some convenient spectral line, from individually unresolved galaxies. An important systematic concern for these
Laporte et al. (2011) reported a very high redshift galaxy candidate: a lensed J-band dropout (A2667-J1). J1 has a photometric redshift of z=9.6-12, the probability density function for which permits no low or intermediate z solution. We here report
We present a new method to estimate redshift distributions and galaxy-dark matter bias parameters using correlation functions in a fully data driven and self-consistent manner. Unlike other machine learning, template, or correlation redshift methods,
The cross-correlation study of the unresolved $gamma$-ray background (UGRB) with galaxy clusters has a potential to reveal the nature of the UGRB. In this paper, we perform a cross-correlation analysis between $gamma$-ray data by the Fermi Large Area
Redshift-space distortions (RSD) in galaxy redshift surveys generally break both the isotropy and homogeneity of galaxy distribution. While the former aspect is particularly highlighted as a probe of growth of structure induced by gravity, the latter