Published February 1, 2026 | Version 1.2
Preprint Open

The χ-Field as Dark Matter: Comprehensive Validation Across 10 Independent Tests with No Per-Galaxy Fit Parameters

Description

This work examines whether a subset of astrophysical phenomena commonly attributed to particle dark matter can instead emerge from spatial structure in a scalar χ field within the Lattice Field Medium (LFM) framework. The dynamics are governed by a single canonical wave equation,

∂²E/∂t² = c²∇²E − χ²(x,t)E

where spatial variations of χ modify local wave propagation and effective inertial response without introducing new particle species.

Theoretical Foundation (New in This Version): We now provide the derivation chain explaining why χ-gradients gravitate. Integrating out short-wavelength E-modes via heat-kernel expansion of the one-loop effective action produces induced operators:

S_ind ⊃ ∫d⁴x√−g { ½M²_ind R + αχ²R + β(∇χ)² + γχ⁴ }

The gradient coupling β ≈ +10⁻³ is positive, meaning χ-gradients carry positive energy density ρ_χ = ½β(∇χ)². This energy gravitates through the modified Poisson equation ∇²Φ = 4πG(ρ_b + ρ_χ), providing the theoretical foundation for the phenomenological enhancement formula. The rotation curve relation v_obs = v_bar × (1 + a₀/a)^0.25 is now derived, not assumed.

The paper focuses on the low-acceleration regime, where gradients and large-scale redistribution of the χ field lead to an effective velocity enhancement relative to Newtonian expectations. An analytic treatment identifies the functional form of this enhancement and shows that, in the deep low-acceleration limit, a baryonic scaling consistent with the observed Tully–Fisher relation arises as an effective description. The characteristic acceleration scale is fixed by cosmological input, a₀ = cH₀/(2π), with no per-galaxy or per-dataset parameter tuning.

Phenomenological χ-dynamics are introduced explicitly as a closure to explore astrophysical consequences; their status and limitations are clearly delineated. The framework is tested against multiple independent observational probes across galactic and extragalactic scales, including galaxy rotation curves (SPARC), baryonic scaling relations, strong gravitational lensing, galaxy cluster mass profiles, the Bullet Cluster, dwarf spheroidals, ultra-diffuse galaxies, wide binaries, and early massive galaxies observed by JWST. Where relevant, qualitative and quantitative comparisons with standard MOND phenomenology are presented under consistent assumptions.

All analyses use published observational datasets and fixed theoretical inputs. Agreements, partial successes, and known tensions are reported explicitly. The results indicate that a collisionless, non-radiating χ-field substrate can reproduce several empirical regularities usually associated with dark matter, while remaining falsifiable in low-acceleration and small-system regimes.

Results: 9 PASS, 1 MARGINAL, 0 FAIL across 10 independent tests.

All scripts, source data, and reproduction instructions are included. This version includes a working Python implementation (lfm_induced_gravity_solver.py) that validates the complete derivation chain from GOV-01 to rotation curves.

Files

LFM-PAPER-044_Chi_Field_Dark_Matter1_2.pdf

Files (3.5 MB)

Name Size Download all
md5:4cd30fe407dc95d48a15189299c2a113
16.8 kB Download
md5:eb85211981d6201972ecf436d65be5e8
295 Bytes Preview Download
md5:a74339f370a4d3ba77feea20d7950404
160.3 kB Preview Download
md5:6bed85eae66bfa49a498bb2bfbadab4c
1.2 kB Preview Download
md5:4b75e19895cce89b0044efc220cf6d22
726 Bytes Download
md5:e3e9f45e86616ecae2ec4f983728a800
1.5 kB Preview Download
md5:e21b3d0182035714f1c8894547ef16df
529 Bytes Preview Download
md5:9e1101700eaabc472479d586d444e85b
2.7 kB Preview Download
md5:174549f0c3df2e62a77780e860e34d43
1.8 kB Preview Download
md5:ab5cd00a508c41a7a2768e92ac829837
10.6 kB Download
md5:65e70bec87029dfa2aca6cde21b15d2d
983 Bytes Preview Download
md5:63f30f0559ba48e67e542ead5c37f01f
168.1 kB Preview Download
md5:909aa2c703d37948a3714a1709a3344f
3.9 kB Preview Download
md5:e5ff7212fad274e53289da6a0be2ae63
8.2 kB Preview Download
md5:648a9531849a5faece8b3f14226bc816
136.3 kB Preview Download
md5:28cd12486f86c2d4afcaad210d6a3020
2.1 kB Preview Download
md5:792c32296f165cfbaf853660d62de885
2.3 kB Preview Download
md5:9ca54df8bd7a8d7d1f1f35e99218ab34
1.4 kB Preview Download
md5:4c4c097ca9cd5878a199a7d083859cac
136.7 kB Preview Download
md5:74879cb79e6a40206e53398a22004971
1.7 kB Preview Download
md5:a5fad4546134e8cb5e58bf1e0e112412
370.9 kB Preview Download
md5:5e80424ab3e0c0fee3babb791055a78b
388.5 kB Preview Download
md5:885d0447c019f4213b17b5911a22112a
20.5 kB Download
md5:88355f3132aefbff6e24b4c504f8e398
1.7 kB Preview Download
md5:effc7ef3bd5a6b861e0b81e9aa4143aa
14.8 kB Download
md5:ecc015f38d6ce3b82622929030091a30
67 Bytes Preview Download
md5:d62cf941e4326feab2bb6b496a658fa9
9.3 kB Download
md5:e1b142488b8dea7085322fe93805742b
15.1 kB Download
md5:77ae9dd60dbbfc31e871529b34c5a76c
2.2 kB Download
md5:16b52d56567dac7ae82045bcbd2abaf2
27.6 kB Download
md5:eaab0cc4b59169cfbe8ce281b1fe6202
11.2 kB Download
md5:5e80424ab3e0c0fee3babb791055a78b
388.5 kB Preview Download
md5:acc15e54ec60d8b53f60674a068b5ace
49.0 kB Download
md5:6eb59a3503d6c35fbcab3a30f45cf4f2
304.5 kB Preview Download
md5:dd6f2eea62daafe01345bbc267a98842
1.9 kB Preview Download
md5:22af32f07829bbaa18c5c66c502ca584
301 Bytes Preview Download
md5:1375aa07b79e85bad05314771843e2e7
153.7 kB Preview Download
md5:c99cff9a28830a2e772ae74c7ea60039
9.1 kB Preview Download
md5:964a7255da7c3a8b6ba1e313e2846c86
286.2 kB Preview Download
md5:aac5e8243db2578a5bec4920dc6a327f
55.4 kB Preview Download
md5:c050e97cf15a9b5a37d10171cf0ee73f
9.1 kB Download
md5:c05301a2d2752f320c93195ab7e936f8
43.8 kB Download
md5:151bbfac261fc5812c07aa8ce45f6b59
9.9 kB Download
md5:5d16d652ec97b984dcb9875c17ef9e2e
1.6 kB Preview Download
md5:0f5209b751f4d5c94ee2e0fde6e3f131
104.8 kB Preview Download
md5:11c8bffcd6c0835458ca6a613ca78848
15.8 kB Download
md5:b97baefb5f74b373185d5c79305841fe
604 Bytes Preview Download
md5:7571c173f61321e6c5cb96ce7f5ae8ae
323 Bytes Preview Download
md5:d04f28fe425e83280180128efd09fc37
14.8 kB Preview Download
md5:9a5fd6e9fe927bf9ab43c7242c3e5de0
2.8 kB Preview Download
md5:1d3ba5786212d31123bfedb97ea5d513
7.8 kB Download
md5:003df9f74030fb3475631cd4452ed34b
16.6 kB Download
md5:8421e1596f06950b041f4c552aaf5020
13.7 kB Download
md5:1e697fc095ef109fef1ff72ea319178a
13.1 kB Download
md5:37c6509f9fbb1159be6ae7f86c7c8fd3
16.2 kB Download
md5:1e730206454b75f20e0ec1fc43234778
14.7 kB Download
md5:ebe3eb6eac4690b5c38e0b72330743bb
13.8 kB Download
md5:2341f4496c09fcb6f7776ec52ae824e1
15.9 kB Download
md5:bfb26a3a5905cb8ee34af942b486fe94
13.0 kB Download
md5:6b68e6cb0be907e58cfc59b8eb167c9f
9.1 kB Download
md5:26a76b0893340b1b911e562a18b9f2f0
15.3 kB Download
md5:00b47dbb0b4389dcede846caeda0759b
22.2 kB Download
md5:7be7e74e81af92eef88654263caa31e3
9.7 kB Download
md5:d6806bf5efe7636b3e101430c72cfbf6
12.2 kB Download
md5:fe9240a087de0b3998c1b944a0f55785
15.5 kB Download
md5:e89ca17ebc40c9eaa1ac186ac608da6f
15.1 kB Download
md5:39776d6f7638e12623eb658bfbd9210e
14.4 kB Download
md5:f58e33e0f1adf544e0b9f52900d2fe91
14.6 kB Download
md5:b23a1aa6d86fef876e143394f77dd470
1.8 kB Preview Download
md5:bee1f8c7aa92e57fabae5d36a2778bdc
112.5 kB Preview Download
md5:dd6fc27c9595f8799309aff7898f64e7
27.7 kB Download
md5:25659ff278b245c33f9c364337a1a30d
95.6 kB Preview Download
md5:79d1ba3ab02ff27877ed474f206c12fa
4.1 kB Preview Download
md5:8b5c7edfeefe9f9acf5c849b61f64657
2.4 kB Preview Download

Additional details

Related works

Cites
Preprint: 10.5281/zenodo.17618474 (DOI)

Software

Programming language
Python