Getting Ready For Analysing Rubin Data : CLMM v2, The Updated LSST-DESC Cluster Weak Lensing Mass Modeling Library For Cosmology
Abstract
The Dark Energy Science Collaboration has prepared a multitude of pipelines to process the upcoming Vera C. Rubin Observatory data and perform different scientific analyses. Here, we present the methodology to constrain the mass information of galaxy clusters from weak lensing, which is encompassed by the v2.0 release of the Cluster Lensing Mass Modeling (CLMM) code. This Python library provides the ingredients for both the modeling and data reduction of lensing observables, and the improvements in the new version were customized to handle the first WL analyses of LSST clusters, including, e.g., stacking functionalities and WL multipoles. This new version of CLMM has allowed various cluster analyses using LSST-DESC simulated data, precursor survey and Rubin commissioning and survey validation data, and is ready to be used for the first cluster cosmology analyses. CLMM is an open-source software package collaboratively written in Python, providing multiple example notebooks, including a set that can reproduce all figures in this work, and extensive documentation, publicly available at https://lsstdesc.org/CLMM/.
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BibTeX
@misc{aguena2026getting,
title = {{Getting Ready For Analysing Rubin Data : CLMM v2, The Updated LSST-DESC Cluster Weak Lensing Mass Modeling Library For Cosmology}},
author = {Michel Aguena and Céline Combet and Shenming Fu and Constantin Payerne and Marina Ricci and Camille Avestruz and Eduardo Barroso and de Oliveira Caio and Zhou Conghao and Hsin Fan and Calum Murray and Anna Niemiec and Nikolina Sarcevic and Tae-hyeon Shin and Rance Solomon and Radhakrishnan Srinivasan and Sunayama Tomomi and Yuanyuan Zhang and Joe Zuntz},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.37455},
eprint = {2610.37455},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.37455},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Getting Ready For Analysing Rubin Data : CLMM v2, The Updated LSST-DESC Cluster Weak Lensing Mass Modeling Library For Cosmology
AU - Michel Aguena
AU - Céline Combet
AU - Shenming Fu
AU - Constantin Payerne
AU - Marina Ricci
AU - Camille Avestruz
AU - Eduardo Barroso
AU - de Oliveira Caio
AU - Zhou Conghao
AU - Hsin Fan
AU - Calum Murray
AU - Anna Niemiec
AU - Nikolina Sarcevic
AU - Tae-hyeon Shin
AU - Rance Solomon
AU - Radhakrishnan Srinivasan
AU - Sunayama Tomomi
AU - Yuanyuan Zhang
AU - Joe Zuntz
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.37455
UR - https://arxiv.org/abs/2610.37455
AB - The Dark Energy Science Collaboration has prepared a multitude of pipelines to process the upcoming Vera C. Rubin Observatory data and perform different scientific analyses. Here, we present the methodology to constrain the mass information of galaxy clusters from weak lensing, which is encompassed by the v2.0 release of the Cluster Lensing Mass Modeling (CLMM) code. This Python library provides the ingredients for both the modeling and data reduction of lensing observables, and the improvements in the new version were customized to handle the first WL analyses of LSST clusters, including, e.g., stacking functionalities and WL multipoles. This new version of CLMM has allowed various cluster analyses using LSST-DESC simulated data, precursor survey and Rubin commissioning and survey validation data, and is ready to be used for the first cluster cosmology analyses. CLMM is an open-source software package collaboratively written in Python, providing multiple example notebooks, including a set that can reproduce all figures in this work, and extensive documentation, publicly available at https://lsstdesc.org/CLMM/.
ER -