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Overview

CLMM (Cluster Lensing Mass Modeling) provides a tool to estimate cluster masses based on weak lensing data. It also includes a routine to make mock catalogs based on cluster_toolkit. CLMM consists of the building blocks for an end-to-end weak lensing cosmology pipeline that can be validated on mock data and run on real data from LSST or other telescopes. We provide examples of its usage in this repository.

Table of contents

  1. The GalaxyCluster object
  2. Weak lensing signal measurement with polaraveraging.py
  3. Profile and cosmology models with modeling.py
  4. Mock data generation
  5. Galaxy cluster mass estimation

The GalaxyCluster object

  • The GalaxyCluster object contains the galaxy cluster metadata (unique_id, ra, dec, z) as well as the background galaxy data
  • Background galaxy data: astropy Table containing at least galaxy_id, ra, dec, e1, e2, z
  • ra/dec are in decimal degrees

Weak lensing signal measurement with polaraveraging.py

  • The function compute_shear calculates tangential shear, cross shear, and angular separation of each source galaxy relative to the (ra, dec) coordinates of the center of the cluster.
  • A shear profile may be constructed with the user's choice of binning via make_bins.
  • make_shear_profile averages the shear of galaxies in each radial bin in rad, deg, arcmin, arcsec, kpc, or Mpc.
  • See examples/demo_polaraveraging_functionality.ipynb for detailed examples.

Profile and cosmology models with modeling.py

  • modeling.py holds functions for evaluating theoretical models.
  • The default is to use an NFW profile, but more halo profile parameterizations will be added soon.
  • See examples/modeling_demo.ipynb for example usage.
  • See examples/demo_modeling_functionality.ipynb for detailed examples.

Mock data generation

Galaxy cluster mass estimation