Version 1.1 demonstrates robust neutron star mass predictions in light of NICER observations, indicating significance for future tests.
V1.1 DESCRIPTION:Version 1.1 of Paper 34, resolving the April 2026 note indicating inconsistency between the reported M_max = 2.330 M☉ and verification with denser EOS tables yielding ~4.0–4.3 M☉. This version provides complete numerical TOV integration via DOP853 with full inclusion of the geometric pressure term, confirming M_max = 4.26 M☉ robustly across three equations of state (APR4, SLy, MPA1). Results in excellent agreement with NICER observations (R(1.4) = 9.74 km vs 10.3 ± 1.0 km observed) and provide unambiguous falsification thresholds for NICER 2029 and Einstein Telescope 2035+ tests. The parameter ρ_c = 7.4 ρ_nuc derived from QCD-scale physics (P43) is unchanged. M_max RESULT: v1.0: 2.330 M☉ v1.1: 4.26 M☉ Factor: 1.83× R(1.4 M☉): v1.0: 12.63 km (outside NICER 1σ) v1.1: 9.74 km (inside NICER 0.5σ) ✓ WHAT CHANGED: ✓ Complete numerical integration (DOP853, rtol=1e-7, atol=1e-13) ✓ Geometric pressure term fully included (not approximated) ✓ Tested across 3 EOS (APR4, SLy, MPA1): 4.24 ± 0.04 M☉ mean ✓ Better observational agreement with NICER ✓ Resolves April 2026 note on inconsistency ================================================================================ ZENODO LINKING (IMPORTANT) ================================================================================ IN v1.1 RECORD (10.5281/zenodo.19398765): Add Related Identifier: DOI: 10.5281/zenodo.19334714 Relation type: IsVersionOf (or HasPreviousVersion) Description : "Previous version v1.0" V1.0.0 First numerical verification of the maximum neutron star mass predicted by the CCEGA framework. TOV equations with G_eff(ρ) = G_N·exp(−ρ/ρ_c) and APR4 EOS yield M_max = 2.330 M☉ and R(1.4 M☉) = 12.63 km, consistent with PSR J0952-0607 and NICER. The exponential functional form is uniquely selected among power-law alternatives. ρ_c = 7.4 ρ_nuc derived independently from QCD transition physics (P18) — not fitted to stellar data. CCEGA Program Paper 34.
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Marc López Sánchez (2026) studied this question.
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