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diff --git a/‎autogalaxy/profiles/mass/dark/ludlow16.py‎
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+"""
+JAX-native Ludlow et al. 2016 mass-concentration relation.
+
+Replaces the previous ``jax.pure_callback``-wrapped call to
+``colossus.halo.concentration`` (formerly in ``mcr_util.py``). The
+algorithm follows colossus' ``modelLudlow16`` (``concentration.py``
+lines 1104-1192) and ``modelEisenstein98`` (``power_spectrum.py``
+lines 476-608) line-for-line, but every operation is done in pure
+``xp.*`` arithmetic so the same function runs under both numpy
+(``xp=np``) and JAX (``xp=jnp``).
+
+Validated in PR #402 (Phase 1 feasibility): max relative error in
+c200c vs colossus over the lensing parameter grid
+(log M ∈ [10, 14] Msun/h, z ∈ [0.1, 2.5]) is **7.5 × 10⁻⁴**, with
+end-to-end downstream errors in convergence/deflection of ≤ 8 × 10⁻⁴.
+The 0.13 dex intrinsic Ludlow16 scatter is ~350× larger.
+
+Units (matching colossus throughout):
+    M200c is in Msun / h
+    R is in Mpc / h
+    z is dimensionless redshift
+"""
+
+import numpy as np
+
+
+# Colossus' 'planck15' preset — the cosmology the previous callback used
+# internally to compute concentration. autogalaxy's own Planck15 (Om0=0.3075)
+# is used for everything else in mcr_util.ludlow16_cosmology; this constant is
+# only for the concentration call. Keeping the split is an apples-to-apples
+# swap with the previous behaviour; unifying the two is a separate decision.
+PLANCK15_COSMOLOGY = dict(
+    h=0.6774,
+    Om0=0.3089,
+    Ob0=0.0486,
+    Tcmb0=2.7255,
+    sigma8=0.8159,
+    ns=0.9667,
+)
+
+
+def _gammainc(a, x, xp):
+    if xp is np:
+        from scipy.special import gammainc
+    else:
+        from jax.scipy.special import gammainc
+    return gammainc(a, x)
+
+
+def _erfc(arg, xp):
+    if xp is np:
+        from scipy.special import erfc
+    else:
+        from jax.scipy.special import erfc
+    return erfc(arg)
+
+
+# ---------------------------------------------------------------------------
+# Eisenstein & Hu 1998 transfer function — direct port of
+# colossus.cosmology.power_spectrum.modelEisenstein98.
+# ---------------------------------------------------------------------------
+
+
+def transfer_eh98(k, h, Om0, Ob0, Tcmb0, xp=np):
+    """EH98 transfer function T(k) including baryon acoustic features."""
+    omc = Om0 - Ob0
+    ombom0 = Ob0 / Om0
+    h2 = h ** 2
+    om0h2 = Om0 * h2
+    ombh2 = Ob0 * h2
+    theta2p7 = Tcmb0 / 2.7
+    theta2p72 = theta2p7 ** 2
+    theta2p74 = theta2p72 ** 2
+
+    kh = k * h
+
+    zeq = 2.50e4 * om0h2 / theta2p74
+    keq = 7.46e-2 * om0h2 / theta2p72
+
+    b1d = 0.313 * om0h2 ** -0.419 * (1.0 + 0.607 * om0h2 ** 0.674)
+    b2d = 0.238 * om0h2 ** 0.223
+    zd = 1291.0 * om0h2 ** 0.251 / (1.0 + 0.659 * om0h2 ** 0.828) * (
+        1.0 + b1d * ombh2 ** b2d
+    )
+
+    Rd = 31.5 * ombh2 / theta2p74 / (zd / 1e3)
+    Req = 31.5 * ombh2 / theta2p74 / (zeq / 1e3)
+
+    s = (
+        2.0
+        / 3.0
+        / keq
+        * xp.sqrt(6.0 / Req)
+        * xp.log((xp.sqrt(1.0 + Rd) + xp.sqrt(Rd + Req)) / (1.0 + xp.sqrt(Req)))
+    )
+
+    ksilk = 1.6 * ombh2 ** 0.52 * om0h2 ** 0.73 * (1.0 + (10.4 * om0h2) ** -0.95)
+
+    q = kh / 13.41 / keq
+
+    a1 = (46.9 * om0h2) ** 0.670 * (1.0 + (32.1 * om0h2) ** -0.532)
+    a2 = (12.0 * om0h2) ** 0.424 * (1.0 + (45.0 * om0h2) ** -0.582)
+    ac = a1 ** (-ombom0) * a2 ** (-(ombom0 ** 3))
+
+    b1 = 0.944 / (1.0 + (458.0 * om0h2) ** -0.708)
+    b2 = (0.395 * om0h2) ** -0.0266
+    bc = 1.0 / (1.0 + b1 * ((omc / Om0) ** b2 - 1.0))
+
+    y = (1.0 + zeq) / (1.0 + zd)
+    Gy = y * (
+        -6.0 * xp.sqrt(1.0 + y)
+        + (2.0 + 3.0 * y)
+        * xp.log((xp.sqrt(1.0 + y) + 1.0) / (xp.sqrt(1.0 + y) - 1.0))
+    )
+
+    ab = 2.07 * keq * s * (1.0 + Rd) ** (-3.0 / 4.0) * Gy
+
+    f = 1.0 / (1.0 + (kh * s / 5.4) ** 4)
+
+    C = 14.2 / ac + 386.0 / (1.0 + 69.9 * q ** 1.08)
+    T0t = xp.log(xp.e + 1.8 * bc * q) / (xp.log(xp.e + 1.8 * bc * q) + C * q * q)
+
+    C1bc = 14.2 + 386.0 / (1.0 + 69.9 * q ** 1.08)
+    T0t1bc = xp.log(xp.e + 1.8 * bc * q) / (
+        xp.log(xp.e + 1.8 * bc * q) + C1bc * q * q
+    )
+    Tc = f * T0t1bc + (1.0 - f) * T0t
+
+    bb = (
+        0.5
+        + ombom0
+        + (3.0 - 2.0 * ombom0)
+        * xp.sqrt((17.2 * om0h2) * (17.2 * om0h2) + 1.0)
+    )
+
+    bnode = 8.41 * om0h2 ** 0.435
+
+    st = s / (1.0 + (bnode / kh / s) * (bnode / kh / s) * (bnode / kh / s)) ** (
+        1.0 / 3.0
+    )
+
+    C11 = 14.2 + 386.0 / (1.0 + 69.9 * q ** 1.08)
+    T0t11 = xp.log(xp.e + 1.8 * q) / (xp.log(xp.e + 1.8 * q) + C11 * q * q)
+    Tb = (
+        T0t11 / (1.0 + (kh * s / 5.2) ** 2)
+        + ab / (1.0 + (bb / kh / s) ** 3) * xp.exp(-((kh / ksilk) ** 1.4))
+    ) * xp.sin(kh * st) / (kh * st)
+
+    return ombom0 * Tb + omc / Om0 * Tc
+
+
+# ---------------------------------------------------------------------------
+# sigma(R, z=0): RMS of mass within top-hat radius R, normalised to sigma8.
+# ---------------------------------------------------------------------------
+
+
+def _tophat_window(x, xp=np):
+    """W(x) = 3 (sin x - x cos x) / x^3, with a safe small-x expansion."""
+    small = x < 1.0e-3
+    x2 = x * x
+    safe_small = 1.0 - x2 / 10.0 + x2 * x2 / 280.0
+    safe_large = 3.0 * (xp.sin(x) - x * xp.cos(x)) / xp.where(small, 1.0, x ** 3)
+    return xp.where(small, safe_small, safe_large)
+
+
+def _sigma2_unnormalised(
+    R, h, Om0, Ob0, Tcmb0, ns,
+    xp=np,
+    k_log_min=-5.0, k_log_max=3.0, nk=256,
+):
+    """sigma^2(R) at z=0 for an unnormalised power spectrum P(k) = k^ns T(k)^2."""
+    ln_k = xp.linspace(
+        k_log_min * xp.log(xp.asarray(10.0)),
+        k_log_max * xp.log(xp.asarray(10.0)),
+        nk,
+    )
+    k = xp.exp(ln_k)
+
+    Tk = transfer_eh98(k, h, Om0, Ob0, Tcmb0, xp=xp)
+    Pk_unnorm = k ** ns * Tk ** 2
+
+    R = xp.atleast_1d(R)
+    kR = k[None, :] * R[:, None]
+    W = _tophat_window(kR, xp=xp)
+
+    integrand = k[None, :] ** 3 * Pk_unnorm[None, :] * W ** 2
+    integrand = integrand / (2.0 * xp.pi ** 2)
+
+    sigma2 = xp.trapezoid(integrand, ln_k, axis=-1)
+    if sigma2.shape == (1,):
+        return sigma2[0]
+    return sigma2
+
+
+def sigma_R(
+    R, h, Om0, Ob0, Tcmb0, sigma8, ns,
+    xp=np,
+    k_log_min=-5.0, k_log_max=3.0, nk=256,
+):
+    """sigma(R, z=0), normalised so sigma(R=8 Mpc/h) = sigma8."""
+    sigma2_unnorm = _sigma2_unnormalised(
+        R, h, Om0, Ob0, Tcmb0, ns,
+        xp=xp, k_log_min=k_log_min, k_log_max=k_log_max, nk=nk,
+    )
+    sigma2_8_unnorm = _sigma2_unnormalised(
+        xp.asarray(8.0), h, Om0, Ob0, Tcmb0, ns,
+        xp=xp, k_log_min=k_log_min, k_log_max=k_log_max, nk=nk,
+    )
+    norm = sigma8 ** 2 / sigma2_8_unnorm
+    return xp.sqrt(norm * sigma2_unnorm)
+
+
+# ---------------------------------------------------------------------------
+# Linear growth factor D(z), flat LCDM (no relspecies).
+# Integral form (Eisenstein & Hu 1999 Eq. 8 / Heath 1977).
+# ---------------------------------------------------------------------------
+
+
+def _E_lcdm(z, Om0, Ode0, xp=np):
+    return xp.sqrt(Om0 * (1.0 + z) ** 3 + Ode0)
+
+
+def _growth_unnormalised(z, Om0, Ode0, xp=np, nz=256, z_max=1.0e4):
+    """D_+(z) un-normalised. Integrate (1+z') / E(z')^3 from z to z_max via u=ln(1+z')."""
+    z_arr = xp.atleast_1d(z).astype(xp.float64)
+
+    u_max = xp.log(xp.asarray(1.0 + z_max))
+    u_low = xp.log(1.0 + z_arr)
+    u_grid = (
+        u_low[:, None]
+        + (u_max - u_low)[:, None] * xp.linspace(0.0, 1.0, nz)[None, :]
+    )
+    zp = xp.exp(u_grid) - 1.0
+    Ep = _E_lcdm(zp, Om0, Ode0, xp=xp)
+    integrand = (1.0 + zp) ** 2 / Ep ** 3
+    integral = xp.trapezoid(integrand, u_grid, axis=-1)
+
+    D = _E_lcdm(z_arr, Om0, Ode0, xp=xp) * integral
+    if z_arr.shape == ():
+        return D[0]
+    return D
+
+
+def growth_factor(z, Om0, Ode0, xp=np, nz=256, z_max=1.0e4):
+    """D(z) / D(0), normalised growth factor for flat LCDM."""
+    D_z = _growth_unnormalised(z, Om0, Ode0, xp=xp, nz=nz, z_max=z_max)
+    D_0 = _growth_unnormalised(xp.asarray(0.0), Om0, Ode0, xp=xp, nz=nz, z_max=z_max)
+    return D_z / D_0
+
+
+# ---------------------------------------------------------------------------
+# Einasto enclosed-mass ratio. For alpha = 0.18 (the value colossus uses
+# internally in modelLudlow16), M(<r_s) / M(<c r_s) = P(3/alpha, 2/alpha) /
+# P(3/alpha, (2/alpha) c^alpha), where P is the regularised lower incomplete
+# gamma function. Independent of cosmology and halo mass.
+# ---------------------------------------------------------------------------
+
+
+_EINASTO_ALPHA = 0.18
+
+
+def einasto_mass_ratio(c, xp=np, alpha=_EINASTO_ALPHA):
+    """M(<r_s) / M(<c r_s) for an Einasto profile, dimensionless."""
+    s = 3.0 / alpha
+    x_inner = 2.0 / alpha
+    x_outer = 2.0 / alpha * c ** alpha
+    return _gammainc(s, x_inner, xp=xp) / _gammainc(s, x_outer, xp=xp)
+
+
+# ---------------------------------------------------------------------------
+# Concentration solver — vectorised port of modelLudlow16.
+# ---------------------------------------------------------------------------
+
+
+_C_LUDLOW = 650.0
+_F_LUDLOW = 0.02
+_DELTA_COLLAPSE = 1.68647019984  # matches colossus.utils.constants.DELTA_COLLAPSE
+
+
+def _lagrangian_R(M, Om0, h, xp=np):
+    """Lagrangian radius for mass M (Msun/h) → R (Mpc/h)."""
+    # Critical density today: 2.77536627e11 Msun h^2 / Mpc^3.
+    rho_crit_0 = 2.77536627e11
+    rho_m_0 = Om0 * rho_crit_0
+    return (3.0 * M / (4.0 * xp.pi * rho_m_0)) ** (1.0 / 3.0)
+
+
+def ludlow16_concentration(
+    M200c_Msun_per_h,
+    z,
+    h,
+    Om0,
+    Ob0,
+    Tcmb0,
+    sigma8,
+    ns,
+    xp=np,
+    Ode0=None,
+    c_array_size=200,
+    sigma_nk=256,
+    growth_nz=256,
+):
+    """
+    JAX-native port of ``colossus.halo.concentration.modelLudlow16``.
+
+    Assumes flat LCDM (``Ode0 = 1 - Om0`` if not supplied) and ignores
+    relativistic species, matching the analytic LCDM branch in colossus.
+
+    Parameters
+    ----------
+    M200c_Msun_per_h : float or scalar xp array
+        Halo mass in Msun/h.
+    z : float or scalar xp array
+        Redshift.
+    h, Om0, Ob0, Tcmb0, sigma8, ns : float
+        Cosmology parameters. See ``PLANCK15_COSMOLOGY`` for the values
+        matching colossus' built-in ``planck15`` preset.
+    xp : module
+        Numerical backend — ``numpy`` or ``jax.numpy``.
+
+    Returns
+    -------
+    c200c : scalar xp array
+    """
+    if Ode0 is None:
+        Ode0 = 1.0 - Om0
+
+    M = xp.asarray(M200c_Msun_per_h, dtype=xp.float64)
+    z = xp.asarray(z, dtype=xp.float64)
+
+    c_array = xp.logspace(0.0, 2.0, c_array_size)
+
+    M_ratio = einasto_mass_ratio(c_array, xp=xp)
+    rho_f_rho_c = 200.0 * c_array ** 3 * M_ratio / _C_LUDLOW
+
+    # Formation redshift (closed-form LCDM); entries with t1 <= 0 are invalid
+    # (low-c, where the formation redshift becomes < -1) and are masked below.
+    t1 = (rho_f_rho_c * (Om0 * (1.0 + z) ** 3 + Ode0) - Ode0) / Om0
+    valid_c = t1 > 0.0
+    t1_safe = xp.where(valid_c, t1, 1.0)
+    zf = t1_safe ** (1.0 / 3.0) - 1.0
+
+    R_fM = _lagrangian_R(_F_LUDLOW * M, Om0, h, xp=xp)
+    R_M = _lagrangian_R(M, Om0, h, xp=xp)
+
+    sigma_fM = sigma_R(R_fM, h, Om0, Ob0, Tcmb0, sigma8, ns, xp=xp, nk=sigma_nk)
+    sigma_M = sigma_R(R_M, h, Om0, Ob0, Tcmb0, sigma8, ns, xp=xp, nk=sigma_nk)
+    sigma2_fM = sigma_fM ** 2
+    sigma2_M = sigma_M ** 2
+
+    D_z = growth_factor(z, Om0, Ode0, xp=xp, nz=growth_nz)
+    delta_z = _DELTA_COLLAPSE / D_z
+    D_zf = growth_factor(zf, Om0, Ode0, xp=xp, nz=growth_nz)
+    delta_zf = _DELTA_COLLAPSE / D_zf
+
+    arg = (delta_zf - delta_z) / xp.sqrt(2.0 * (sigma2_fM - sigma2_M))
+    rhs = _erfc(arg, xp=xp)
+
+    # Solve M_ratio - rhs == 0 along c. Colossus trims c_array to entries
+    # with t1 > 0 then np.interp; the un-trimmed array must remain monotonic
+    # increasing in lhs_rhs for xp.interp. Pin invalid entries (low c) below
+    # the lowest valid lhs_rhs (∈ [-1, 1]) so they sit at the bottom of xp.
+    lhs_rhs = M_ratio - rhs
+    lhs_rhs = xp.where(valid_c, lhs_rhs, -10.0)
+
+    return xp.interp(0.0, lhs_rhs, c_array)