A Unified Quantum Gravity Framework with Bounce Solutions ( SECOND PUBLİCATİON )

We release an exploratory **bounce-plus-inflation** framework with runnable code. The pipeline integrates the background through an LQC-style effective bounce, computes scalar/tensor spectra with simple quantum-correction knobs, and then provides an **optional EFT-style calibration** at a pivot scale. With calibration, the model **matches** Planck-consistent tilt and a representative tensor amplitude (**n_s ≈ 0.965**, **r ≈ 0.0077**). We include scripts for sensitivity scans and **uncertainty quantification** with a seeded RNG; when not calibrating, **r** is handled with a **one-sided upper limit** (the summary reports the fraction of samples below the chosen limit). **Scope & limitations.** “Matched” values are **calibrated**, not first-principles predictions. Quantum-correction elements are phenomenological knobs. Any comments about black-hole interiors, echoes, or H₀/σ₈ should be read as **speculative outlook** unless supported by methods in the package. **Heuristic entropy proxy.** We report a **Shannon-entropy proxy** from the scalar spectrum: - Convert the (nonnegative) spectrum into weights on the sampled k-bins, w_i = P_s(k_i)/∑_j P_s(k_j). - Compute S = -∑ w_i ln w_i (natural logs). Also report S_norm = S/ln(N) in [0,1]. This is a **diagnostic proxy of spectral spread**, *not* a thermodynamic entropy nor a claim about information preservation. **How to reproduce (three commands).** bash # 1) Calibrated observables (match {n_s, r} at pivot) python - <<'PY' from complete_bounce_solution__3_ import CompleteBounceModel m = CompleteBounceModel() o = m.predict_observables(calibrate=True, target_ns=0.965, target_r=0.0077, params={"phi0":10.0, "t_max":2.0}, n_k=60) print({k: o[k] for k in ("n_s","r","A_s","pivot")}) PY # 2) UQ (seeded). Use --calibrate to get descriptive stats around matched values. python uncertainty_quantification__1_.py --n-samples 10000 --seed 42 --calibrate # 3) Entropy proxy (heuristic) python - <<'PY' from complete_bounce_solution__3_ import CompleteBounceModel import numpy as np import sensitivity_analysis__3_ as SA m = CompleteBounceModel() o = m.predict_observables(calibrate=True, target_ns=0.965, target_r=0.0077, params={"phi0":10.0, "t_max":2.0}, n_k=60) print("S, S_norm =", SA.entropy_proxy(o["k"], o["P_s"], normalize=True)) PY **Files in this version (key).** - complete_bounce_solution__3_.py — full pipeline with predict_observables(...) + analytic dV/dφ - uncertainty_quantification__1_.py — calls the pipeline; seeded RNG; one-sided r handling when uncalibrated - sensitivity_analysis__3_.py — entropy proxy and helper **License.** CC-BY-4.0 (or compatible).