# Peak profiles This chapter gives the shape and width of one peak, and how the instrument’s width and the sample’s combine. ## 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 [(3.8)](#equation-prof-pv). The opt-in exact shape is the closed form of [(3.14)](#equation-prof-voigt). The one convolution this chapter does integrate is the axial divergence of [(3.19)](#equation-prof-fcj-integral). ⊕ 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. $$ \Gamma_G^2(\theta) \;=\; (U + U_s)\tan^2\theta + V\tan\theta + W + \frac{P}{\cos^2\theta} \qquad [\deg^2 2\theta] $$ *Source:* [`rietx.model.profiles.caglioti.gaussian_fwhm`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/caglioti.py#L280) $U, V, W$ are the instrument resolution function [[Caglioti *et al.*, 1958](references.html.md#id28)]. 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](references.html.md#id15); [Thompson *et al.*, 1987](references.html.md#id29)]. $\Gamma_G^2$ is a variance, so the physical constraint is on the sum and not on its coefficients: $$ \Gamma_G^2(\theta) \;>\; 0 \qquad \text{for every } \theta \text{ in the fitted range} $$ *Source:* [`rietx.strategy.staged.check_resolution_positive`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/strategy/staged.py#L1711) 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. $W$ has a floor of its own. Its lower bound is the clamp itself, $\varepsilon$ = 1e-08 deg², and not 0. Under the clamp every Gaussian width column is exactly zero. The default Levenberg-Marquardt driver puts a parameter exactly on its bound when the data push it there ([Estimation](estimation.html.md#ch-estimation), § Solvers). With a floor of 0, a stage freeing $W$ while $U$ and $V$ were near 0 put $\Gamma_G^2$ under the clamp at every point, and the stage stopped there. With $U$ and $V$ at 0, $W$ on its floor holds $\Gamma_G^2$ at $\varepsilon$, and a step up in $W$ still widens every peak. A stored or passed lower bound under the floor is raised to it when the profile is built, and a value under it is raised with it. GSAS-II clamps the same way, but its solver takes no bounds, so no step lands on a floor there. The floor holds $W$ and not the sum. A negative $U$ or $V$ can still take $\Gamma_G^2$ under the clamp. So the driver also holds the sum on a single-histogram fit. Each reflection on the data adds one row to its linear inequalities ([Estimation](estimation.html.md#ch-estimation), § Solvers): $$ \Gamma_G^2(\theta_{hkl}) \;\ge\; \varepsilon \qquad \text{for every reflection } hkl \text{ on the data} $$ *Source:* [`rietx.optimize.least_squares.gaussian_width_inequalities`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/optimize/least_squares.py#L1888) $\Gamma_G^2$ here includes the phase’s Gaussian size and strain terms, and the angles are those of the cell at the start of the stage. TRF and a joint fit build no rows, and neither does a reflection that starts under the clamp. The guard above stays the check for those. $$ \Gamma_L(\theta) \;=\; \frac{X + X_s}{\cos\theta} + (Y + Y_s)\tan\theta \qquad [\deg 2\theta] $$ *Source:* [`rietx.model.profiles.caglioti.lorentzian_fwhm`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/caglioti.py#L316) $\Gamma_L$ is a width, so it must not go below zero. $X$ has a floor of 0, because as $\theta \to 0$ the $\tan\theta$ term vanishes and the instrument’s $\Gamma_L$ is $X$ itself. $Y$ has no floor of its own. A negative $Y$ is legitimate wherever $X$ carries the width. The default driver holds the sum on a single-histogram fit, one more row per reflection on the data: $$ \Gamma_L(\theta_{hkl}) \;\ge\; 0 \qquad \text{for every reflection } hkl \text{ on the data} $$ *Source:* [`rietx.optimize.least_squares.lorentzian_width_inequalities`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/optimize/least_squares.py#L1992) $\Gamma_L$ here includes the phase’s Lorentzian size and strain terms. An anisotropic strain block adds a term its own cone keeps at or above zero, so the row leaves it out. TRF builds no rows. Below zero the pseudo-Voigt mixing parameter turns negative and the line is no longer a peak shape, so `RESOLUTION_NOT_POSITIVE` reports where the instrument’s $\Gamma_L$ falls below zero in the fitted range. > **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](microstructure.html.md#sec-width-as-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](microstructure.html.md#ch-microstructure). ### 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](references.html.md#id9)]. 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 paper finds the same three parameters well 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. ## 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: $$ Y_s \;\le\; Y_{\max}, \qquad U_s \;\le\; Y_{\max}^2, \qquad Y_{\max} \;=\; \frac{f\,(2\theta_{\max} - 2\theta_{\min})}{\tan\theta_{\max}} \quad [\deg 2\theta] $$ *Source:* [`rietx.params.vector.strain_cap`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/params/vector.py#L788) 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 [(3.6)](#equation-prof-strain-cap) 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). ## 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. A line whose size contribution alone is wider than the interval it was measured over carries no information the fit could have got from the data. So the strain rule applies with $1/\cos\theta$ in place of $\tan\theta$, evaluated where $1/\cos\theta$ is largest: $$ X_s \;\le\; f\,(2\theta_{\max}-2\theta_{\min})\cos\theta_{\max} \quad [\deg 2\theta] $$ *Source:* [`rietx.params.vector.size_cap`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/params/vector.py#L924) with $f$ = 1.0 the range fraction. $P$ is a variance and takes the square of the same width. On a Cu Kα 10–80° scan the bound is ≈ 54 deg. Scherrer’s relation, [(6.3)](microstructure.html.md#equation-ms-size-coefficient) in [Reading a width as a size](microstructure.html.md#sec-width-as-size), reads that coefficient as a crystallite of ≈ 0.15 nm, so the bound limits a runaway and not a size. > **Warning** > > No default bound limits the crystallite size itself. A floor of 2 nm sat > beside this bound until 1.10. It was removed because a small crystallite is > still a physical quantity, and a limit on one is yours to state with a finite > `Parameter.max`. Below about 2 nm a crystallite is a few unit cells, and > broadened Bragg peaks may not describe its pattern. The Debye scattering > equation does. The `SIZE_UNUSUALLY_SMALL` diagnostic fires below > 5.0 nm, says so, and bounds nothing. TOPAS’s `CS_L` macro > limits size to 0.3-10 000 nm, and GSAS-II clamps it to 0.001-10 µm after each > refinement. FullProf sets no limit. The bound behaves as the [strain cap](#sec-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 below the bound is bit-identical to an unbounded build. ## Thompson-Cox-Hastings pseudo-Voigt TCH is Thompson, Cox and Hastings, whose 1987 paper [[Thompson *et al.*, 1987](references.html.md#id29)] 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)](#equation-prof-caglioti-g) and [(3.4)](#equation-prof-caglioti-l), 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 $\eta$. Its argument is the offset $x = 2\theta - 2\theta_k$ from reflection $k$’s Bragg position: $$ \mathrm{pV}(x) \;=\; \eta\, L(x; \Gamma) + (1 - \eta)\, G(x; \Gamma), $$ $$ \begin{aligned} \Gamma^5 = \Gamma_G^5 &+ 2.69269\, \Gamma_G^4 \Gamma_L + 2.42843\, \Gamma_G^3 \Gamma_L^2 \\ &+ 4.47163\, \Gamma_G^2 \Gamma_L^3 + 0.07842\, \Gamma_G \Gamma_L^4 + \Gamma_L^5, \end{aligned} $$ $$ \eta = 1.36603\, q - 0.47719\, q^2 + 0.11116\, q^3, \qquad q = \Gamma_L / \Gamma. $$ *Source:* [`rietx.model.profiles.pseudovoigt.tch_gamma_eta`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/pseudovoigt.py#L37) ### 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. [(3.9)](#equation-prof-tch-gamma) 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](references.html.md#id29)]. [(3.10)](#equation-prof-tch-eta) 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](references.html.md#id30)], and the mixing expansion is Hastings, Thomlinson and Cox’s [[Hastings *et al.*, 1984](references.html.md#id31)]. TCH renormalised that expansion’s coefficients for the unit-area form of [(3.8)](#equation-prof-pv), 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 tune. Neither paper quotes an accuracy for its expansion, so the accuracy of the pair is measured here against the exact convolution rietx ships as [(3.14)](#equation-prof-voigt). Across the whole range $0 \le q \le 1$, with the true FWHM found by bisecting the Faddeeva Voigt (`tests/test_voigt.py`): * [(3.9)](#equation-prof-tch-gamma) 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)](forward-model.html.md#equation-fm-rietveld) enters purely through the prefactor: $$ G(x) = \frac{2}{\Gamma}\sqrt{\frac{\ln 2}{\pi}} \exp\!\left(-\frac{4 \ln 2\, x^2}{\Gamma^2}\right), \qquad L(x) = \frac{2/(\pi\Gamma)}{1 + 4x^2/\Gamma^2}. $$ *Source:* [`rietx.model.profiles.pseudovoigt.pseudo_voigt`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/pseudovoigt.py#L52) ## 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 [(3.8)](#equation-prof-pv), one image per emission line: $$ y_{\mathrm{peak}}(2\theta) = A \sum_{l} g_l\, \mathrm{pV}\!\left(2\theta - 2\theta_l;\ \Gamma,\ \eta\right), \qquad g_l = w_l\, \frac{\mathrm{Lp}(2\theta_l)}{\mathrm{Lp}(2\theta_0)}, $$ with $2\theta_l$ the Bragg image of the declared apparent centre $2\theta_0$, $$ \sin\theta_l = \frac{\lambda_l}{\lambda_0}\,\sin\theta_0 . $$ *Source:* [`rietx.model.forward.CompiledModel.extra_peak_curve`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/forward.py#L965) The intensity enters as an area $A$, in counts·deg. That is what a reflection intensity is, and what the unit-area normalisation of [(3.11)](#equation-prof-components) 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 [(3.19)](#equation-prof-fcj-integral) 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. [(3.12)](#equation-prof-extra-peak) 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](method.html.md#ch-method) 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](forward-model.html.md#ch-forward) 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. ## 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](references.html.md#id33)]: $$ V(x; \sigma, \gamma) \;=\; \frac{\operatorname{Re}[w(z)]}{\sigma\sqrt{2\pi}}, \qquad z = \frac{x + i\gamma}{\sigma\sqrt{2}}, $$ *Source:* [`rietx.model.profiles.voigt.voigt`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/voigt.py#L62) 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)](#equation-prof-caglioti-g)-[(3.4)](#equation-prof-caglioti-l). This is the one place in the manual where a width is something other than an FWHM, and the conversion is the equation: $$ \sigma = \frac{\Gamma_G}{2\sqrt{2\ln 2}}, \qquad \gamma = \frac{\Gamma_L}{2}. $$ *Source:* [`rietx.model.profiles.voigt.fwhm_to_voigt_params`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/voigt.py#L40) Both limits are exact and recovered without a branch: $\gamma \to 0$ 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](references.html.md#id34)]: the conformal map of the upper half-plane onto the unit disc, $$ Z = \frac{L + iz}{L - iz}, \qquad L = \sqrt[4]{1/2}\cdot\sqrt{N}, \qquad w(z) = \frac{2\, p(Z)}{(L - iz)^2} + \frac{1/\sqrt{\pi}}{L - iz}, $$ *Source:* [`rietx.model.profiles.faddeeva.faddeeva_w`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/faddeeva.py#L81) 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](references.html.md#id35)]) and no series switching [[Zaghloul and Ali, 2011](references.html.md#id36)]. 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](references.html.md#id37)]. ## 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](references.html.md#id32)] $$ \cos 2\varphi \;=\; \cos 2\theta \cdot \sqrt{1 + \xi^2}, \qquad \xi = u/L, $$ *Source:* [`rietx.model.profiles.fcj.fcj_offsets_weights`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/fcj.py#L152) 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 $s = S/L$, $h = H/L$): $$ W(\xi) \;=\; \operatorname{clip}\bigl(s + h - \xi,\; 0,\; 2\min(s, h)\bigr), \qquad \xi \ge 0. $$ *Source:* [`rietx.model.profiles.fcj`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/fcj.py#L1) 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: $$ y(2\theta_i) \;=\; \frac{\int_0^{\xi_{\max}} W(\xi)\, \Omega\bigl(2\theta_i - 2\varphi(\xi)\bigr)\, d\xi} {\int_0^{\xi_{\max}} W(\xi)\, d\xi}, \qquad \xi_{\max} = \min\bigl(s + h,\; |\tan 2\theta|\bigr), $$ *Source:* [`rietx.model.profiles.fcj`](https://github.com/yue-here/rietx/blob/v1.10.0/src/rietx/model/profiles/fcj.py#L1) 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, and node positions follow $s$, $h$ and $2\theta$ smoothly. A declared stage takes 6.0 nodes per FWHM of smear where the map moves fastest, clamped to [8, 64]. When the asymmetric extent falls below 0.02 of the peak FWHM, it treats the peak as symmetric. A compile at windows finer than the declared stages’ takes 12.0 nodes per FWHM, clamped to [10, 1024], and treats a peak as symmetric below 0.0003. The closing stage and any model evaluated outside a fit are such compiles. That rule keeps the composite peak within a tenth of the counting noise of a 512-node reference on lab and synchrotron peaks. The declared stages’ rule misses it by up to eight times on a lab Lorentzian, and those stages only steer. With one aperture at zero the trapezoid [(3.18)](#equation-prof-fcj-weight) has no height, and [(3.19)](#equation-prof-fcj-integral) is a ratio of two zeros. Its limit as $h \to 0^+$ with $s > 0$ is a uniform weight over $[0, \xi_{\max}]$, because $W/2h \to 1$ there and the falling edge shrinks to nothing. rietx evaluates that limit, so the profile is continuous over the whole range of apertures. Only $s = h = 0$ gives the symmetric peak. A refinement can therefore land an aperture exactly on its lower bound of zero. The quadrature is split at the kink of the trapezoid [(3.18)](#equation-prof-fcj-weight), 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](method.html.md#ch-method) works them through.