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This issue is dedicated to the creation of an example notebook for the use of the ClusterEnsemble object with DC2 data. We consider using a sub-sample of redMaPPer-detected cluster (\sim 400 objects) and we estimate their lensing signal using cosmoDC2 galaxies.
With this notebook we will provide source code to extract background source catalogs and estimate individual lensing profiles. The example notebook will load the individual profiles and will make use of the ClusterEnsemble object to estimate the cluster sample stacked lensing profile and covariance matrix.
We will also provide individual lensing profiles already extracted, since it takes some time to do it.
_utils_cosmoDC2.py has a few useful functions to handle cosmoDC2 data, especially for the extraction of lensing catalogs using Qserv or GCRCatalogs at CC-IN2P3.
jobs_extract_sources_cosmoDC2.py that writes and runs parallel jobs to speed up the extraction of lensing catalogs for a stack of 450 clusters (around 30 minutes).
DC2_data gathers the different DC2 datasets (redMaPPer cluster catalog and individual lensing profiles).
This issue is dedicated to the creation of an example notebook for the use of the ClusterEnsemble object with DC2 data. We consider using a sub-sample of redMaPPer-detected cluster (\sim 400 objects) and we estimate their lensing signal using cosmoDC2 galaxies.
With this notebook we will provide source code to extract background source catalogs and estimate individual lensing profiles. The example notebook will load the individual profiles and will make use of the ClusterEnsemble object to estimate the cluster sample stacked lensing profile and covariance matrix.
We will also provide individual lensing profiles already extracted, since it takes some time to do it.
This issue is addressed in the branch /issue/637/clusterensemble_dc2
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