3. Peak profiles

3.1. The instrument ⊕ sample width split

⊗ is convolution. A measured line is the specimen’s line convolved with the instrument’s, so the shape to approximate is a Voigt, a Gaussian ⊗ Lorentzian. That convolution is never evaluated as an integral. The default profile approximates it by the linear blend of (3.6). The opt-in exact shape is the closed form of (3.12). The one convolution this chapter does integrate is the axial divergence of (3.17).

⊕ is shorthand for the rule convolution imposes on the widths. The Gaussian and Lorentzian component widths each carry an instrument part and a sample part. Gaussian variances add, and Lorentzian FWHMs add. So “instrument ⊕ sample” means a specimen contribution combined with an instrument contribution by that rule, and never two numbers of degrees added together.

The two widths below are full widths at half maximum, in deg 2θ. The Gaussian one is written as a law for its square, because the variance is what adds, and that is where its deg² come from.

(3.1)\[\Gamma_G^2(\theta) \;=\; (U + U_s)\tan^2\theta + V\tan\theta + W + \frac{P}{\cos^2\theta} \qquad [\deg^2 2\theta]\]

Source: rietx.model.profiles.caglioti.gaussian_fwhm

\(U, V, W\) are the instrument resolution function [Caglioti et al., 1958]. The sample adds a Gaussian microstrain term \(U_s\tan^2\theta\) and a Gaussian size term \(P/\cos^2\theta\) [Larson and Von Dreele, 2004; Thompson et al., 1987].

\(\Gamma_G^2\) is a variance, so the physical constraint is on the sum and not on its coefficients:

(3.2)\[\Gamma_G^2(\theta) \;>\; 0 \qquad \text{for every } \theta \text{ in the fitted range}\]

Source: rietx.strategy.staged.check_resolution_positive

Each coefficient separately may be negative. A negative \(V\) is the ordinary sign of a focusing geometry, and the schema bounds allow it. What the sum cannot do is go below zero where a peak is being fitted, because the width there would be the square root of a negative variance.

Nothing raises when it does. gaussian_fwhm clamps \(\Gamma_G^2\) to a small floor to keep the root real, so the model reports a resolution some four orders finer than any goniometer and the fit goes on converging. The condition above is therefore checked as a guard rather than enforced as a bound, and a fit that trips it reports RESOLUTION_NOT_POSITIVE. Read that as “these resolution parameters are not quotable”, the same reading STEPHENS_STRAIN_NOT_POSITIVE has and for the same reason: a variance left its physical set, and the widths built on it are not measurements.

(3.3)\[\Gamma_L(\theta) \;=\; \frac{X + X_s}{\cos\theta} + (Y + Y_s)\tan\theta \qquad [\deg 2\theta]\]

Source: rietx.model.profiles.caglioti.lorentzian_fwhm

Warning

Conventions here are documented by physics and not by letters. The \(1/\cos\theta\) terms (and the \(1/\cos^2\theta\) variance term) carry Scherrer crystallite-size broadening, and the \(\tan\theta\) terms (and \(\tan^2\theta\)) carry microstrain. The letter assignments differ between codes. GSAS uses X = size and Y = strain, and FullProf swaps them. Transfer a value by matching the \(\theta\)-law, never the letter.

Reading a width as a size reads either \(1/\cos\theta\) coefficient back as a crystallite size, and says why \(U\) and \(W\) have no size to read. Anisotropic (hkl-dependent) sample broadening is in Microstructure.

3.1.1. When \(U\), \(V\) and \(W\) are determined at all

The two laws above are fitted together, so how well each is determined depends on how much of the peak it carries. On high-resolution data the peaks are predominantly Lorentzian, and there the Gaussian parameters are poorly constrained: “unconstrained refinement of the Gaussian parameters \(U\), \(V\) and \(W\) may lead to nonphysical results, or at worst, complete failure of the refinement” [McCusker et al., 1999]. The same source gives the remedy, and it is not a limit on the width. Apply a constraint, or hold the parameters at the instrumental values.

The converse holds for constant-wavelength neutron data, where the instrument dominates the profile and the same three parameters are, in that paper’s words, easily determined by refinement. So the question is about the character of the pattern rather than the size of the widths, and any single bound on width would have to be wrong for one technique or the other.

rietx therefore reports rather than bounds. A fit that frees \(U\), \(V\) or \(W\) on a pattern where \(\Gamma_L\) exceeds \(\Gamma_G\) at more than half the fitted points gets a RESOLUTION_UNCONSTRAINED diagnostic. “Predominantly” is a comparison between two computed widths, so no calibrated constant enters. The workflow that implements the paper’s remedy is in Part 1: calibrate on a standard with its certified cell held fixed, save the profile, and load it before the sample fit.

3.2. The strain width bound

Nothing in \(Y_s\tan\theta\) stops at a physical strain. Past some width the phase’s lines are flat across the whole scan. They are then degenerate with the background, they starve whatever they overlap, and the covariance loses conditioning. Measured on a 248-pattern in-situ reduction series, \(Y_s\) reached \(1.1\times10^{5}\) where the reference protocol for the same data used 0.3.

\(Y_s\) therefore carries a default upper bound derived from the pattern itself rather than from any material, and so does \(U_s\), the same width squared:

(3.4)\[Y_s \;\le\; Y_{\max}, \qquad U_s \;\le\; Y_{\max}^2, \qquad Y_{\max} \;=\; \frac{f\,(2\theta_{\max} - 2\theta_{\min})}{\tan\theta_{\max}} \quad [\deg 2\theta]\]

Source: rietx.params.vector.strain_cap

with \(2\theta_{\min}, 2\theta_{\max}\) the ends of the fitted range (excluded regions removed, since an excluded interval was not measured). \(f\) is the fraction of that range one term is allowed to spend, and rietx sets it to 1.0. \(U_s\) takes the square because it is a variance. At \(f = 1\) the strain term alone would contribute one whole range’s worth of FWHM at the pattern’s highest \(\theta\), so the bound says that a line wider than the interval it was measured over is not a line. The rule is dimensional and self-scaling. A 15–80° lab scan and a 0.5–50° low-angle scan get different bounds out of it, and no calibrated constant enters.

Warning

This bounds the numerics and not the strain. It sits two orders of magnitude above anything a specimen produces (≈ 83 deg on a 10–80° scan), so it makes no judgement about whether a width is believable. That judgement is the STRAIN_UNUSUALLY_LARGE diagnostic, which fires above 1.5 deg, the top of a corpus of 606 solved refinements, and bounds nothing. Nanocrystalline and heavily defective specimens refine into that tail legitimately. Treat a flag there as a number to check.

The bound is applied at the optimiser interface for one stage, and never on the stored parameter. Two properties follow.

• It is armed only on a term that has already reached it. A finite bound changes the trust-region step in a coordinate even where nothing approaches it, so a fit whose strain stays inside (3.4) gets no bound at all and is bit-identical to an unbounded build.

• A finite stored max is the caller’s claim and outranks it, by the rule the per-stage cell window follows. Declaring any ceiling switches the default off, a deliberately enormous one included.

Enforcement is per stage rather than per iteration. A stage that starts below the bound may cross it, and the next stage pulls it back and reports BOUND_HIT on the path (RefinedParameter.at_bound carries the same finding on the row).

3.3. The size coefficient bound

The size terms \(X_s/\cos\theta\) and \(P/\cos^2\theta\) have the same runaway geometry as the strain terms and take the same fix. The intent differs, and so does the number. A large \(X_s\) is a small crystallite (via Scherrer, (6.3) in Reading a width as a size), and real specimens are genuinely nanocrystalline, so this bound is set to catch a runaway and not to legislate a size:

(3.5)\[X_s \;\le\; \min\!\left( \frac{(180/\pi)\,K\lambda}{L_{\min}},\;\; f\,(2\theta_{\max}-2\theta_{\min})\cos\theta_{\max} \right) \quad [\deg 2\theta]\]

Source: rietx.params.vector.size_cap

with \(L_{\min}\) the smallest crystallite the bound admits, 2.0 nm, and \(f\) the range fraction of (3.4).

The first term is a floor on the crystallite (\(L \ge L_{\min}\)), read as a ceiling on the coefficient with no reference angle, (6.3) in Reading a width as a size, per wavelength: ≈ 4°/cos θ at Cu Kα for 2 nm. The second is the strain rule’s range backstop, with \(1/\cos\theta\) in place of \(\tan\theta\), evaluated where \(1/\cos\theta\) is largest. On any real scan the floor is far the tighter (Cu 10–80°: ≈ 4 deg against ≈ 54 deg), so the floor governs, and the backstop catches only the \(1/\cos\theta\) pathology near 2θ = 180°. \(P\) is a variance and takes the square of whichever width binds.

Warning

\(L_{\min}\) makes no claim about how small a crystallite may be. It is a runaway fence two unit cells below the archive. The smallest well-determined size in a corpus of 606 solved refinements is ≈ 33 nm, and every refined size below ~2 nm there is an exploratory or placeholder fit. A genuinely nano specimen refines freely above the fence. For one truly smaller than 2 nm, declare the bound you mean: a finite Parameter.max outranks the default. The surprising-but-possible band is the SIZE_UNUSUALLY_SMALL diagnostic, which fires below 5.0 nm and bounds nothing.

The bound behaves as the strain cap does. It is armed only on a term that has already reached it, a finite stored max outranks it, and it reports BOUND_HIT when the next stage pulls a crossing back. Any fit that stays off the floor is bit-identical to an unbounded build.

3.4. Thompson-Cox-Hastings pseudo-Voigt

TCH is Thompson, Cox and Hastings, whose 1987 paper [Thompson et al., 1987] supplies the two polynomials below. The trailing Z of the class name ProfileTCHZ is not theirs. That paper’s own width model is one parameter per component, \(\Gamma_G = V\tan\theta\) and \(\Gamma_L = X/\cos\theta\), with no \(U\), \(W\) or \(Y\) and nothing called \(Z\). Every further letter came from the codes that adopted the profile afterwards, the label included, and those codes disagree on which term wears which. rietx’s class holds the five coefficients \(U, V, W, X, Y\) of (3.1) and (3.3), so read the \(\theta\)-law and not the label, as the chapter’s first warning already says for X and Y. TCH’s own two letters do survive it. Their \(V\) is this chapter’s Gaussian \(\tan\theta\) term, and their \(X\) this chapter’s Lorentzian \(1/\cos\theta\) one.

The default profile approximates the Voigt (Gaussian ⊗ Lorentzian) as a linear blend with a single FWHM \(\Gamma\) [deg 2θ] and a mixing fraction \(\eta\):

(3.6)\[\mathrm{pV}(x) \;=\; \eta\, L(x; \Gamma) + (1 - \eta)\, G(x; \Gamma),\]

(3.7)\[\begin{split}\begin{aligned} \Gamma^5 = \Gamma_G^5 &+ 2.69269\, \Gamma_G^4 \Gamma_L + 2.42843\, \Gamma_G^3 \Gamma_L^2 \\ &+ 4.47163\, \Gamma_G^2 \Gamma_L^3 + 0.07842\, \Gamma_G \Gamma_L^4 + \Gamma_L^5, \end{aligned}\end{split}\]

(3.8)\[\eta = 1.36603\, q - 0.47719\, q^2 + 0.11116\, q^3, \qquad q = \Gamma_L / \Gamma.\]

Source: rietx.model.profiles.pseudovoigt.tch_gamma_eta

3.4.1. Origin and accuracy of the coefficients

The coefficients are fitted rather than derived, because a Voigt has no closed-form width and no exact pseudo-Voigt equivalent. The two equations do not have the same author.

(3.7) is TCH’s, and the paper gives its origin in one clause: “another simple series expansion derived from a set of computer-generated convolutions” [Thompson et al., 1987]. (3.8) is older. The pseudo-Voigt as a way of reading a line’s Gaussian and Lorentzian content is Wertheim, Butler, West and Buchanan’s [Wertheim et al., 1974], and the mixing expansion is Hastings, Thomlinson and Cox’s [Hastings et al., 1984]. TCH renormalised that expansion’s coefficients for the unit-area form of (3.6), and say so in the paper. The digits are transcribed from two papers. None of them carries physics on its own, and none is a rietx constant to retune.

Neither paper quotes an accuracy for its expansion, so the accuracy of the pair is measured here against the exact convolution rietx ships as (3.12). Across the whole range \(0 \le q \le 1\), with the true FWHM found by bisecting the Faddeeva Voigt (tests/test_voigt.py):

• (3.7) reproduces the true Voigt FWHM to within 0.43 %, worst near \(\Gamma_L \approx \Gamma_G/2\), and is exact in both pure limits;

• the pseudo-Voigt built from the pair departs from the exact Voigt by at most 1.3 % of the peak height, worst at \(q \approx 0.56\).

Where that 1.3 % sits matters more than its size. At the peak centre the departure stays under 0.25 % across the whole range. The worst of it is on the flanks, at \(x \approx \pm0.28\,\Gamma\), where a peak’s position and width derivatives live. The choice between "tchz_pv" and "voigt" is therefore not a choice about peak heights. The exact shape has something to offer on a pattern whose lines are neither nearly Gaussian nor nearly Lorentzian.

Both component shapes are unit-area normalised, so \(\int \mathrm{pV}\,dx = 1\) and the reflection intensity of (1.2) enters purely through the prefactor:

(3.9)\[G(x) = \frac{2}{\Gamma}\sqrt{\frac{\ln 2}{\pi}} \exp\!\left(-\frac{4 \ln 2\, x^2}{\Gamma^2}\right), \qquad L(x) = \frac{2/(\pi\Gamma)}{1 + 4x^2/\Gamma^2}.\]

Source: rietx.model.profiles.pseudovoigt.pseudo_voigt

3.5. Declared extra peaks

A sample holder, a mount or an unidentified impurity may diffract the same source as the specimen and put a sharp line where no phase in the model has one. Such a line has no cell behind it, so it is declared rather than derived. It is evaluated as a single unit-area pseudo-Voigt of (3.6), one image per emission line:

(3.10)\[y_{\mathrm{peak}}(2\theta) = A \sum_{l} g_l\, \mathrm{pV}\!\left(2\theta - 2\theta_l;\ \Gamma,\ \eta\right), \qquad g_l = w_l\, \frac{\mathrm{Lp}(2\theta_l)}{\mathrm{Lp}(2\theta_0)},\]

with \(2\theta_l\) the Bragg image of the declared apparent centre \(2\theta_0\),

(3.11)\[\sin\theta_l = \frac{\lambda_l}{\lambda_0}\,\sin\theta_0 .\]

Source: rietx.model.forward.CompiledModel.extra_peak_curve

The intensity enters as an area \(A\), in counts·deg. That is what a reflection intensity is, and what the unit-area normalisation of (3.9) makes the prefactor mean. The line gain \(g_l\) carries the line’s weight and the two lines’ Lorentz-polarisation ratio. Each image diffracts at its own Bragg angle and so carries its own Lp, and holding the bare weight instead biases the fitted primary position by a measured amount. Line 0 is the primary, with \(w_0 \equiv 1\) and \(g_0 = 1\), so \(A\) is the primary line’s area.

The profile deliberately carries three things less than a reflection does.

• No position correction of the specimen’s reaches \(2\theta_0\). Zero shift, displacement and transparency all describe where the specimen sits, and a holder at its own distance has aberrations of its own. The free centre absorbs them.

• No axial asymmetry. The Finger-Cox-Jephcoat convolution (3.17) describes the specimen’s axial geometry, so a symmetric pseudo-Voigt is the honest model for an intruder.

• No structure factor, multiplicity or Lorentz factor beyond the line ratio, because there is no cell to compute them from.

(3.10) therefore describes a feature and does not predict one.

The evaluation window is frozen per stage, as every window in Reading a paper against its own numbers is, and it is sized from the declared bounds rather than from the current values. Its half-width is \(k(\eta_{\max})\,\Gamma_{\max} + \Delta\), with \(k\) the area-tolerance multiplier of The forward model and \(\Delta\) the same absolute movement slack a reflection window carries. A centre free anywhere inside its bounds is therefore inside its frozen window by construction.

3.6. The true Voigt, via the Faddeeva function

An opt-in shape (Instrument.profile.shape = "voigt"; TCHZ stays the default) evaluates the exact convolution [Armstrong, 1967]:

(3.12)\[V(x; \sigma, \gamma) \;=\; \frac{\operatorname{Re}[w(z)]}{\sigma\sqrt{2\pi}}, \qquad z = \frac{x + i\gamma}{\sigma\sqrt{2}},\]

Source: rietx.model.profiles.voigt.voigt

where \(\sigma\) is the Gaussian standard deviation and \(\gamma\) the Lorentzian half-width at half maximum, both in deg 2θ. Both are recovered from the component FWHMs of (3.1)-(3.3). This is the one place in the manual where a width is something other than an FWHM, and the conversion is the equation:

(3.13)\[\sigma = \frac{\Gamma_G}{2\sqrt{2\ln 2}}, \qquad \gamma = \frac{\Gamma_L}{2}.\]

Source: rietx.model.profiles.voigt.fwhm_to_voigt_params

Both limits are exact and recovered branchlessly: \(\gamma \to 0\) makes \(z\) real and \(\operatorname{Re}[w] = e^{-z^2}\) (the unit Gaussian); \(\sigma \to 0\) sends \(|z| \to \infty\) where \(w(z) \to i/(\sqrt{\pi} z)\) (the unit Lorentzian).

The Faddeeva function \(w(z) = e^{-z^2}\operatorname{erfc}(-iz)\) (\(\operatorname{Im} z \ge 0\)) is computed by the Weideman rational approximation [Weideman, 1994]: the conformal map of the upper half-plane onto the unit disc,

(3.14)\[Z = \frac{L + iz}{L - iz}, \qquad L = \sqrt[4]{1/2}\cdot\sqrt{N}, \qquad w(z) = \frac{2\, p(Z)}{(L - iz)^2} + \frac{1/\sqrt{\pi}}{L - iz},\]

Source: rietx.model.profiles.faddeeva.faddeeva_w

with \(p\) an \(N\)-term polynomial whose real coefficients come from a single FFT at import time. \(N = 32\) reaches ≈1e-13. The algorithm is branchless over the whole upper half-plane, with no region partition (Humlíček’s w4 [Humlíček, 1982]) and no series switching [Zaghloul and Ali, 2011]. A branchless form keeps the residual smooth for finite-difference and autodiff Jacobians, and that is what this one was chosen for. The Voigt argument always has \(\operatorname{Im} z = \gamma_L/(\sigma\sqrt{2}) \ge 0\), so the reflection formula for the lower half-plane never arises. Derivatives reuse the same \(w\) call through the identity \(w'(z) = -2z\,w(z) + 2i/\sqrt{\pi}\) [Abramowitz and Stegun, 1964].

3.7. Finger-Cox-Jephcoat axial divergence

With a sample of axial half-length \(S\) and a receiving slit of axial half-length \(H\) at goniometer radius \(L\), rays leaving the diffraction plane are detected at an apparent angle \(2\varphi\) related to the true Bragg angle by [Finger et al., 1994]

(3.15)\[\cos 2\varphi \;=\; \cos 2\theta \cdot \sqrt{1 + \xi^2}, \qquad \xi = u/L,\]

Source: rietx.model.profiles.fcj.fcj_offsets_weights

where \(u\) is the signed axial offset of the ray. For \(2\theta < 90°\) intensity smears from \(2\theta\) down to \(2\varphi_{\min}\), the classic low-angle tail of laboratory data. Above 90° the smear is toward high angle. The weight of a given offset is the axial overlap of sample and slit, a trapezoid in \(\xi\) (with \(s = S/L\), \(h = H/L\)):

(3.16)\[W(\xi) \;=\; \operatorname{clip}\bigl(s + h - \xi,\; 0,\; 2\min(s, h)\bigr), \qquad \xi \ge 0.\]

Source: rietx.model.profiles.fcj

As a density in \(2\varphi\), the aberration diverges like \(1/\sqrt{2\theta - 2\varphi}\) at the Bragg position. Naive sampling fails on that divergence. Substituting \(\xi\) as the integration variable removes the singularity exactly:

(3.17)\[y(2\theta_i) \;=\; \frac{\int_0^{\xi_{\max}} W(\xi)\, \Omega\bigl(2\theta_i - 2\varphi(\xi)\bigr)\, d\xi} {\int_0^{\xi_{\max}} W(\xi)\, d\xi}, \qquad \xi_{\max} = \min\bigl(s + h,\; |\tan 2\theta|\bigr),\]

Source: rietx.model.profiles.fcj

The integrand is smooth, and fixed-node Gauss-Legendre quadrature in \(\tau = \xi/\xi_{\max}\) evaluates it. The \(|\tan 2\theta|\) cap removes the unphysical \(\cos 2\varphi > 1\) branch at very low angle. The weights are renormalised to \(\sum\omega = 1\), so the composite peak keeps unit area and reflection intensities stay areas. Node counts are frozen per stage (6.0 nodes per FWHM of smear where the map moves fastest, clamped to [8, 64]), and node positions follow \(s\), \(h\) and \(2\theta\) smoothly. When the asymmetric extent falls below 0.02 of the peak FWHM the aberration is invisible, and the peak is treated as symmetric.

The quadrature is split at the kink of the trapezoid (3.16), which keeps the response \(C^1\) everywhere but the inherent FCJ corner at \(s = h\). That corner is a genuine non-differentiability, and it has measured consequences for refinement when both apertures are equal. Reading a paper against its own numbers works them through.