Sparse Bayesian mass-mapping with uncertainties: hypothesis testing of structure. (arXiv:1812.04014v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Price_M/0/1/0/all/0/1">Matthew A. Price</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+McEwen_J/0/1/0/all/0/1">Jason D. McEwen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cai_X/0/1/0/all/0/1">Xiaohao Cai</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kitching_T/0/1/0/all/0/1">Thomas D. Kitching</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wallis_C/0/1/0/all/0/1">Christopher G. R. Wallis</a> (for the LSST Dark Energy Science Collaboration)
A crucial aspect of mass-mapping, via weak lensing, is quantification of the
uncertainty introduced during the reconstruction process. Properly accounting
for these errors has been largely ignored to date. We present results from a
new method that reconstructs maximum a posteriori (MAP) convergence maps by
formulating an unconstrained Bayesian inference problem with Laplace-type
$ell_1$-norm sparsity-promoting priors, which we solve via convex
optimization. Approaching mass-mapping in this manner allows us to exploit
recent developments in probability concentration theory to infer theoretically
conservative uncertainties for our MAP reconstructions, without relying on
assumptions of Gaussianity. For the first time these methods allow us to
perform hypothesis testing of structure, from which it is possible to
distinguish between physical objects and artifacts of the reconstruction. Here
we present this new formalism, demonstrate the method on illustrative examples,
before applying the developed formalism to two observational datasets of the
Abel-520 cluster. In our Bayesian framework it is found that neither Abel-520
dataset can conclusively determine the physicality of individual local massive
substructure at significant confidence. However, in both cases the recovered
MAP estimators are consistent with both sets of data.
A crucial aspect of mass-mapping, via weak lensing, is quantification of the
uncertainty introduced during the reconstruction process. Properly accounting
for these errors has been largely ignored to date. We present results from a
new method that reconstructs maximum a posteriori (MAP) convergence maps by
formulating an unconstrained Bayesian inference problem with Laplace-type
$ell_1$-norm sparsity-promoting priors, which we solve via convex
optimization. Approaching mass-mapping in this manner allows us to exploit
recent developments in probability concentration theory to infer theoretically
conservative uncertainties for our MAP reconstructions, without relying on
assumptions of Gaussianity. For the first time these methods allow us to
perform hypothesis testing of structure, from which it is possible to
distinguish between physical objects and artifacts of the reconstruction. Here
we present this new formalism, demonstrate the method on illustrative examples,
before applying the developed formalism to two observational datasets of the
Abel-520 cluster. In our Bayesian framework it is found that neither Abel-520
dataset can conclusively determine the physicality of individual local massive
substructure at significant confidence. However, in both cases the recovered
MAP estimators are consistent with both sets of data.
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