data for paper "Spatially Structured Dissipation Generates Quantum Coherence and Coherence-Driven Transport in Open Fermion Systems"

"Spatially Structured Dissipation Generates Quantum Coherence and Coherence-Driven Transport in Open Fermion Systems"==================================================================== GENERAL CONVENTIONS-------------------All quantities are dimensionless. Physical parameters are set to: gamma0 = 1.0 (uniform dephasing rate) L = 1.0 (box length) N_K = 16384 (DFT grid for propagator computation) The stripped propagator is rho_tilde_r(t) = exp(-xi0*t) * I_r(xi0*t),where I_r is the modified Bessel function of the first kind andxi0 = 2*gamma0*eps is the coherence-space coupling scale. The universal rate function is: Phi_GE(chi) = chi * arcsinh(chi) - sqrt(1 + chi^2) + 1 The self-consistent diffusion scale: xi0_eff = 2 * sum_k k^2 * J_k FILE DESCRIPTIONS----------------- fig2a_bessel_kernel.csv Fig 2 panels (a,b): Bessel kernel verification for sinusoidal model. Parameters: eps=0.20, t=80, xi0t=16. Columns: r, rho_tilde_r_num/rho_tilde_0, I_r/I_0, |residual| fig2c_rate_function_Phi.csv Fig 2 panel (c): Universal rate function Phi_GE(chi). Columns: chi, Phi_GE fig3_convergence.csv Fig 3: Convergence of F(chi;t) to Phi_GE(chi) for sinusoidal model. Parameters: eps=0.12, xi0t in {6,12,24,60,240}. Columns: xi0t, chi, F(chi;t), Phi_GE(chi) fig4_scgf.csv Fig 4: Normalized SCGF mu_eff(theta) for five models. Parameters: eps=0.15. Columns: theta, cosh(theta)-1, mu_eff for each model fig5a_rate_functions.csv Fig 5 panel (a): Model-specific rate functions Phi_gen(chi). Parameters: eps=0.15, Legendre transform with self-consistent xi0. Columns: chi, Phi_GE, Phi_gen for each model fig5b_rate_fn_deviation_vs_J2J1.csv Fig 5 panel (b): |Phi_gen - Phi_GE| vs J2/J1 at fixed chi. Parameters: eps=0.15. Columns: J2/J1, deviation at chi=0.3, 0.6, 1.0, 1.4 fig6_universal_collapse.csv Fig 6: Universal collapse F(chi;t)->Phi_GE for five dissipation profiles. Parameters: eps=0.15, t=300. Columns: model_name, xi0t, chi, F(chi;t), Phi_GE(chi) fig7_heatmap_phase_diagram.csv Fig 7: BCDC universality phase diagram (32x32 log-spaced grid). Parameters: eps=0.15, t=280, 32x32 log-spaced grid. Columns: J2/J1, J3/J1, RMS_residual fig8_ldp_per_model.csv Fig 8: LDP F(chi;t)->Phi_gen(chi) for four models. Parameters: eps=0.15, t=350. Columns: model_name, xi0t, chi, value, Phi_GE(chi), data_type data_type: "rate_fn" = Legendre result; "propagator" = numerical F(chi;t) fig9a_convergence_rate.csv Fig 9 panel (a): |F(chi;t) - Phi_GE(chi)| vs xi0t (power-law convergence). Parameters: eps=0.15, sinusoidal model. Columns: xi0t, chi, |F-Phi_GE|, F(chi;t), Phi_GE(chi) fig9b_scaling_collapse.csv Fig 9 panel (b): Scaling collapse A(chi) = |F-Phi|*(xi0t)^0.5. Parameters: eps=0.15, xi0t in {20,50,100,200,500,1000}. Columns: xi0t, chi, A(chi), |F-Phi| fig10a_finitesize_profiles.csv Fig 10 panel (a): Propagator profiles for N=64..1024 and infinite line. Parameters: eps=0.20, t=60, xi0t=12. N=inf denotes the infinite-line result. Columns: r, N, rho_tilde_r/rho_tilde_0 fig10b_finitesize_residual.csv Fig 10 panel (b): Finite-size residual vs N (exponential suppression). Parameters: eps=0.20, xi0t=8, chi=0.80. Columns: N, |rho_r^(N)/rho_0 - I_r(xi0t)/I_0(xi0t)| phi_GE_comparison.csv Comparison of exact Phi_GE with approximate formulas. Columns: chi, Phi_GE, Phi_approx (chi log chi - chi + 1), Phi_asymp (chi log(2chi) - chi + 1), |Phi_GE - Phi_approx| REPRODUCIBILITY---------------All data can be regenerated by running data_export.py with: Python >= 3.10 NumPy, SciPy The code is self-contained and uses only the parameters stated hereand in the individual file headers.