Exposé
court
Résumé
:Over the past few years, clustering-based redshift estimation has
emerged as a new way to estimate redshifts and perform extragalactic
tomography of arbitrary datasets. On a similar timescale, observations
by Planck, WISE, Pan-STARRS and 21cm radio surveys have been used to
create a multitude of SFD-type Galactic dust maps. I will explain how
clustering-based redshift estimation can be used to test the quality of
the seven different dust maps currently available and I will show that
extragalactic signatures can be revealed in many of them. When such
maps are used for correcting optical magnitudes, we therefore expect
biases which are likely to affect the precision of cosmological
experiments using supernovae, BAOs, or the growth of structures. I will
present possible solutions to alleviate this issue and discuss which
map should be used depending on which measurement one wishes to make.
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