A model-agnostic observational framework for testing kinematic-history dependence in gravitational lensing

The Dark Sector Memory Test is a model-agnostic observational framework for testing whether gravitational lensing encodes kinematic history beyond the instantaneous matter distribution.

Multiple theoretical frameworks predict "memory" effects in the dark sector:

The key question: Do lensing convergence residuals correlate with merger infall velocity?

Despite theoretical predictions, no systematic observational test has been performed — until now.

We have analyzed three merging galaxy clusters with Hubble Frontier Fields convergence maps and published kinematic constraints:

Plane-of-sky mergers show a positive correlation between infall velocity and residual dipole moment:

Abell 370 deviates from this trend — but this is physically expected: its merger is largely along the line of sight, so any wake signature would be foreshortened in projection.

• For plane-of-sky mergers, higher infall velocity → larger dipole moment and asymmetry
• This is consistent with wake signature predictions from superfluid DM and nonlocal gravity
• Line-of-sight mergers require projection corrections before inclusion

• Abell 2146 (v = 2700 km/s, Mach = 2.3 ± 0.2) — best-constrained kinematics from shock measurements (Russell+ 2012). Convergence map requested from Coleman/King.
• With 3+ plane-of-sky mergers, we can compute statistically meaningful correlations

1. Reconstructs lensing convergence maps from HST weak+strong lensing data
2. Constructs ΛCDM baseline models via parametric fitting (Gaussian smoothing)
3. Computes residual maps: Δκ = κ_obs − κ_baseline
4. Measures six morphological metrics quantifying residual structure
5. Tests for correlations between metrics and published merger kinematics

git clone https://github.com/LBCplus/Dark-Sector-Memory-Test.git cd Dark-Sector-Memory-Test pip install -r requirements.txt

# Download Frontier Fields convergence maps from MAST python code/download_and_analyze.py --download --data-dir ./data

python code/download_and_analyze.py --analyze --data-dir ./data

Dark-Sector-Memory-Test/ ├── README.md # You are here ├── LICENSE # MIT License ├── CITATION.cff # Citation metadata ├── requirements.txt # Python dependencies │ ├── paper/ │ ├── dsmt_paper_draft.md # Manuscript draft │ └── figures/ # Publication figures │ ├── code/ │ ├── dsmt_analysis.py # Main analysis module (~700 lines) │ └── download_and_analyze.py # Data pipeline with published kinematics │ ├── docs/ │ ├── methodology.md # Detailed methods │ └── statistical_analysis_plan.md # Pre-specified analysis │ ├── configs/ │ └── pilot_study.yaml # Analysis configuration with kinematic params │ └── data/ # Data directory (not tracked) └── .gitkeep 

We quantify lensing residual structure using six metrics:

• MACSJ0416: Jauzac et al. 2015, MNRAS 446, 4132
• Abell 2744: Chadayammuri et al. 2024, arXiv:2407.03142 (2.1 Ms Chandra + JWST)
• Abell 370: Bimodal galaxy redshift distribution (~3000 km/s separation)
• Abell 2146: Russell et al. 2012, MNRAS 423, 236 (shock Mach numbers)

Standard ΛCDM predicts that lensing depends only on the current matter distribution. Several beyond-ΛCDM theories predict dependence on kinematic history:

Superfluid Dark Matter (Berezhiani & Khoury 2015)

"Merger dynamics depend on the infall velocity versus phonon sound speed; distinct mass peaks in bullet-like cluster mergers correspond to superfluid and normal components."

Sivakumar et al. (2025) — Most direct prediction:

"Merger-induced turbulence should produce asymmetric, fine-structure residuals in lensing maps, correlated with infall velocity."

Non-Markovian EFT (Chaudhuri et al. 2025)

Memory kernels from integrated-out heavy fields produce history-dependent gravitational response.

Nonlocal Gravity (Maggiore & Mancarella 2014)

Past stress-energy contributes to present gravitational dynamics via retarded Green's functions.

Cognola et al. (2022) tested nonlocal gravity against cluster lensing and found it indistinguishable from GR using standard methods, concluding that "a different discriminator is needed."

DSMT is that discriminator.

• Berezhiani & Khoury (2015) — Superfluid DM framework — PRD 92, 103510
• Sivakumar et al. (2025) — Turbulent mergers — PRD 111, 083511
• Chaudhuri et al. (2025) — Non-Markovian EFT — arXiv:2509.22293
• Maggiore & Mancarella (2014) — Nonlocal gravity — PRD 90, 023005

• Grayson et al. (2024) — MACSJ0416 BUFFALO model — MNRAS 536, 2690
• Russell et al. (2012) — Abell 2146 kinematics — MNRAS 423, 236
• Chadayammuri et al. (2024) — Abell 2744 multiwavelength — arXiv:2407.03142
• Coleman et al. (2017) — Abell 2146 strong lensing — MNRAS 464, 2469

• Cognola et al. (2022) — Nonlocal gravity vs. GR degeneracy — arXiv:2205.03216

Contributions welcome! Particularly interested in:

• Additional cluster convergence maps with published kinematic constraints
• Simulation comparisons (TNG-Cluster, BAHAMAS)
• Statistical methodology improvements
• Theoretical predictions from other frameworks

Please open an issue or submit a pull request.

If you use this code or methodology, please cite:

@software{dsmt2026, author = {[Author]}, title = {Dark Sector Memory Test: Probing Dark Matter and Dark Energy History-Dependence via Cluster Mergers}, year = {2026}, url = {https://github.com/LBCplus/Dark-Sector-Memory-Test} }

This project is licensed under the MIT License — see LICENSE for details.

• HST Frontier Fields and BUFFALO teams for public lensing data
• MAST archive for data hosting
• Chandra X-ray Observatory for archival data
• The lensing community for published convergence maps

Testing whether gravity remembers