A blind method to recover the mask of a deep galaxy survey. (arXiv:1812.02104v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Monaco_P/0/1/0/all/0/1">Pierluigi Monaco</a> (1, 2, 3), <a href="http://arxiv.org/find/astro-ph/1/au:+Dio_E/0/1/0/all/0/1">Enea Di Dio</a> (4, 5), <a href="http://arxiv.org/find/astro-ph/1/au:+Sefusatti_E/0/1/0/all/0/1">Emiliano Sefusatti</a> (2) ((1) University of Trieste; (2) INAF-OATs; (3) INFN Trieste; (4) LBL, Berkeley; (5) Berkeley Center for Cosmological Physics)
We present a blind method to determine the properties of a foreground
contamination, given by a visibility mask, that affects a deep galaxy survey.
Angular cross correlations of density fields in different redshift bins are
expected to vanish (apart from a contribution due to lensing), but are
sensitive to the presence of a foreground that modulates the flux limit across
the sky. After formalizing the expected effect of a foreground mask on the
measured galaxy density, under a linear, luminosity-dependent bias model for
galaxies, we construct two estimators that single out the mask contribution if
a sufficient number of independent redshift bins is available. These estimators
are combined to give a reconstruction of the mask. We use Milky-Way reddening
as a prototype for the mask. Using a set of 20 large mock catalogs covering
$1/4$-th of the sky and number-matched to $Halpha$ emitters to mimic an
Euclid-like sample, we demonstrate that our method can reconstruct the mask and
its angular clustering at scales $ell<100$, beyond which the cosmological
signal becomes dominant. The uncertainty of this reconstruction is quantified
to be $1/3$-rd of the sample variance of the signal. Such a reconstruction
requires knowledge of the average and square average of the mask, but we show
that it is possible to recover this information either from external models or
internally from the data. It also relies on knowledge of how the impact of the
foreground changes with redshift (due to the extinction curve in our case), but
this can be tightly constrained by cross correlations of different redshift
bins. The strong points of this blind reconstruction technique lies in the
ability to find ``unknown unknowns'' that affect a survey, and in the facility
to quantify, using sets of mock catalogs, how its uncertainty propagates to
clustering measurements. [Abridged]
We present a blind method to determine the properties of a foreground
contamination, given by a visibility mask, that affects a deep galaxy survey.
Angular cross correlations of density fields in different redshift bins are
expected to vanish (apart from a contribution due to lensing), but are
sensitive to the presence of a foreground that modulates the flux limit across
the sky. After formalizing the expected effect of a foreground mask on the
measured galaxy density, under a linear, luminosity-dependent bias model for
galaxies, we construct two estimators that single out the mask contribution if
a sufficient number of independent redshift bins is available. These estimators
are combined to give a reconstruction of the mask. We use Milky-Way reddening
as a prototype for the mask. Using a set of 20 large mock catalogs covering
$1/4$-th of the sky and number-matched to $Halpha$ emitters to mimic an
Euclid-like sample, we demonstrate that our method can reconstruct the mask and
its angular clustering at scales $ell<100$, beyond which the cosmological
signal becomes dominant. The uncertainty of this reconstruction is quantified
to be $1/3$-rd of the sample variance of the signal. Such a reconstruction
requires knowledge of the average and square average of the mask, but we show
that it is possible to recover this information either from external models or
internally from the data. It also relies on knowledge of how the impact of the
foreground changes with redshift (due to the extinction curve in our case), but
this can be tightly constrained by cross correlations of different redshift
bins. The strong points of this blind reconstruction technique lies in the
ability to find “unknown unknowns” that affect a survey, and in the facility
to quantify, using sets of mock catalogs, how its uncertainty propagates to
clustering measurements. [Abridged]
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