Cluster Lensing Mass Inversion (CLUMI+): Combining Dynamics and Weak Lensing around Galaxy Clusters
Bibliographic record
Abstract
Abstract We present clumi+, a self-consistent, multiprobe methodology for reconstructing the mass distribution in and around galaxy clusters by combining gravitational lensing and dynamical observations. Building on the joint likelihood framework of K. Umetsu, clumi+ integrates weak-lensing shear and magnification data with projected escape velocity measurements in the cluster infall region, yielding tighter constraints on the gravitational potential without relying on equilibrium assumptions. The mass distribution is modeled using a flexible, piecewise-defined convergence profile that characterizes the azimuthally averaged surface mass density within the lensing field, transitioning to a projected power-law form at larger radii where phase-space constraints complement lensing. Additional strong-lensing constraints are incorporated via central aperture-mass measurements, enabling full-scale mass reconstruction from the cluster core to the outskirts. We validate clumi+ using synthetic weak-lensing and phase-space data for a massive cluster from the IllustrisTNG simulations, demonstrating unbiased recovery of projected and three-dimensional mass profiles and achieving 10%–30% improvement in precision at large radii. As a case study, we apply clumi+ to A2261, combining Subaru and Hubble Space Telescope weak+strong-lensing data with spectroscopic measurements from the Hectospec Cluster Survey. This analysis demonstrates the power of multiprobe, equilibrium-free modeling for robust cluster mass reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".