Kavli Affiliate: Anthony Challinor
| First 5 Authors: Íñigo Zubeldia, Boris Bolliet, Anthony Challinor, William Handley,
| Summary:
The abundance of galaxy clusters as a function of mass and redshift is a
well-established and powerful cosmological probe. Cosmological analyses based
on galaxy cluster number counts have traditionally relied on explicitly
computed likelihoods, which are often challenging to develop with the required
accuracy and expensive to evaluate. In this work, we implement an alternative
approach based on simulation-based inference (SBI) methods that relies solely
on synthetic galaxy cluster catalogues generated under a given model. These
catalogues are much easier to produce than it is to develop and validate a
likelihood. We validate this approach in the context of the galaxy cluster
survey of the upcoming Simons Observatory for a setup in which we can also
evaluate an exact explicit likelihood. We find that our SBI-based approach
yields cosmological parameter posterior means that are within $0.2,sigma$ of
those obtained with the explicit likelihood and with biases smaller than
$0.1,sigma$. We also introduce and validate a procedure to assess the
goodness of fit using only synthetic catalogues similar to those used for
training. This demonstrates, for the first time, that a galaxy cluster number
count cosmological analysis can be performed fully without resorting to a
likelihood at any stage. Finally, we apply our SBI-based approach to the real
Planck MMF3 cosmology sample, obtaining cosmological parameter constraints that
are within $0.1,sigma$ of their likelihood-based counterparts. This
constitutes the first SBI-based number count cosmological analysis of a real
galaxy cluster catalogue.
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