clmm 1.14.40

A comprehensive package for galaxy cluster weak lensing

CLMM

The LSST-DESC Cluster Lensing Mass Modeling (CLMM) code is a DESC tool consisting of a Python library for performing galaxy cluster mass reconstruction from weak lensing observables. CLMM is associated with Key Tasks DC1 SW+RQ and DC2 SW of the LSST-DESC Science Roadmap pertaining to absolute and relative mass calibration.

The documentation of the code can be found here and the overview of the code can be found here. The journal paper that describes the development and validation of CLMM v1.0 can be found here. If you make use of the ideas or software here, please cite that paper and provide a link to this repository: https://github.com/LSSTDESC/CLMM. Please follow the guidelines listed below to install, use and contribute to CLMM.

Table of contents

1. Installing CLMM
2. Using CLMM
3. Contributing to CLMM
4. Contact
5. Acknowledgements

Installing CLMM

CLMM can be installed with pip or conda. There commands will install most of the dependencies for CLMM, with the exception of the backend to be used on theoretical predictions, check this section for details.

For a pip installation, run:

    pip install clmm

For a conda installation, run:

    conda install -c conda-forge clmm

We highly recommend you make a new conda environment for the installation of CLMM, see INSTALL documentation for instructions on how to do it.

Requirements

CLMM requires Python version greater than 3.8 and at maximum 3.12.

Dependencies

CLMM has the following dependencies:

• NumPy (v1.17 or later)
• SciPy (v1.6 or later)
• Astropy (v4.0 or later for units and cosmology dependence) (Please avoid Astropy v5.0 since there is bug breaking CCL backend. It has been fixed in Astropy v5.0.1.)
• Matplotlib (for plotting and going through tutorials)
• Healpy
• qp (Higher python versions than 3.12 may have conflicts with this package for different system distributions)

  pip install numpy scipy astropy matplotlib healpy qp-prob

Back-ends

For the theoretical predictions of the signal, CLMM relies on existing libraries and at least one of the following must be installed as well:

• cluster-toolkit
• CCL (versions between 2.7.1.dev10+gf81b59a4 and 3)
• NumCosmo (v0.19 or later)

(See the INSTALL documentation for more detailed installation instructions.)

Developers

For developers, you will also need to install:

• pytest (3.x or later for testing)
• Sphinx (for documentation)

These are also pip installable:

  pip install pytest sphinx sphinx_rtd_theme

Note, the last item, sphinx_rtd_theme is to make the docs.

Manual installation

To install CLMM manually, you need to build it from source:

  git clone https://github.com/LSSTDESC/CLMM.git
  cd CLMM
  python setup.py install --user   # Add --user flag to install it locally

See the INSTALL documentation for more detailed installation instructions.

To run the tests you can do:

pytest

Using CLMM

This code has been released by DESC, although it is still under active development. You are welcome to re-use the code, which is open source and available under terms consistent with our LICENSE (BSD 3-Clause). In this case, don't forget to reference the paper and the repository. If you use CLMM for a project, please see the guidelines below, depending on your case.

DESC Projects: External contributors and DESC members wishing to use CLMM for DESC projects should consult with the DESC Clusters analysis working group (CL WG) conveners, ideally before the work has started, but definitely before any publication or posting of the work to the arXiv.

Non-DESC Projects by DESC members: If you are in the DESC community, but planning to use CLMM in a non-DESC project, it would be good practice to contact the CL WG co-conveners and/or the CLMM Topical Team leads as well (see Contact section). A desired outcome would be for your non-DESC project concept and progress to be presented to the working group, so working group members can help co-identify tools and/or ongoing development that might mutually benefit your non-DESC project and ongoing DESC projects.

External Projects by Non-DESC members: If you are not from the DESC community, you are also welcome to contact CLMM Topical Team leads to introduce your project and share feedback.

For free use of the NumCosmo library, the NumCosmo developers require that the NumCosmo publication be cited: NumCosmo: Numerical Cosmology, S. Dias Pinto Vitenti and M. Penna-Lima, Astrophysics Source Code Library, record ascl:1408.013. See citation info here. The NumCosmo repository can be found here.

For free use of the CCL library, the CCL developers require that the CCL publication be cited. See details here.

The Cluster Toolkit documentation can be found here.

The data for the notebook test_coordinate.ipynb is available at https://www.dropbox.com/scl/fo/dwsccslr5iwb7lnkf8jvx/AJkjgFeemUEHpHaZaHHqpAg?rlkey=efbtsr15mdrs3y6xsm7l48o0r&st=xb58ap0g&dl=0

Contributing to CLMM

You are welcome to contribute to the code. To do so, please follow the guidelines described here. If you are not part of the DESC CLMM topical team, it is good to also contact us (see below).

Contact

If you have comments, questions, or feedback, please write us an issue.

The current leads of the LSST DESC CLMM Topical Team are Michel Aguena (m-aguena, [e-mail]) and Marina Ricci (mricci, [e-mail])

Acknowledgements

The DESC acknowledges ongoing support from the Institut National de Physique Nucl'eaire et de Physique des Particules in France; the Science & Technology Facilities Council in the United Kingdom; and the Department of Energy, the National Science Foundation, and the LSST Corporation in the United States. DESC uses resources of the IN2P3 Computing Center (CC-IN2P3--Lyon/Villeurbanne - France) funded by the Centre National de la Recherche Scientifique; the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231; STFC DiRAC HPC Facilities, funded by UK BIS National E-infrastructure capital grants; and the UK particle physics grid, supported by the GridPP Collaboration. This work was performed in part under DOE Contract DE-AC02-76SF00515.

The authors express gratitude to the LSSTC for the 2018 and 2019 Enabling Science grants, hosted by CMU and RUB respectively, that supported the development of CLMM and its developer community. CA acknowledges support from the LSA Collegiate Fellowship at the University of Michigan, the Leinweber Foundation, and DoE Award DE-FOA-0001781. AIM acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. During the completion of this work, AIM was advised by David W. Hogg and supported by National Science Foundation grant AST-1517237. CS acknowledges support from the Agencia Nacional de Investigaci'on y Desarrollo (ANID) through FONDECYT grant no. 11191125. AvdL, RH, LB, and HF acknowledge support by the US Department of Energy under award DE-SC0018053. SF acknowledges support from DOE grant DE-SC0010010. HM is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.