Index: src/sas/sasgui/perspectives/fitting/media/fitting_help.rst
===================================================================
--- src/sas/sasgui/perspectives/fitting/media/fitting_help.rst	(revision 6aad2e8b144e92be94b299c8ae7f4f418c094f1d)
+++ src/sas/sasgui/perspectives/fitting/media/fitting_help.rst	(revision 5ed76f8390eac56ff5d3779fd9c454d0c8fe1dc0)
@@ -381,5 +381,5 @@
 
 In the bottom left corner of the *Fit Page* is a box displaying the normalised value
-of the statistical |chi|\  :sup:`2` parameter returned by the optimiser.
+of the statistical $\chi^2$ parameter returned by the optimiser.
 
 Now check the box for another model parameter and click *Fit* again. Repeat this
@@ -387,5 +387,5 @@
 fit of the theory to the experimental data improves the value of 'chi2/Npts' will
 decrease. A good model fit should easily produce values of 'chi2/Npts' that are
-close to zero, and certainly <100. See :ref:`Assessing_Fit_Quality`.
+close to one, and certainly <100. See :ref:`Assessing_Fit_Quality`.
 
 SasView has a number of different optimisers (see the section :ref:`Fitting_Options`).
Index: src/sas/sasgui/perspectives/fitting/media/mag_help.rst
===================================================================
--- src/sas/sasgui/perspectives/fitting/media/mag_help.rst	(revision 6aad2e8b144e92be94b299c8ae7f4f418c094f1d)
+++ src/sas/sasgui/perspectives/fitting/media/mag_help.rst	(revision 5ed76f8390eac56ff5d3779fd9c454d0c8fe1dc0)
@@ -20,5 +20,5 @@
 --------------------------------
 
-Magnetic scattering is implemented in five (2D) models 
+Magnetic scattering is implemented in five (2D) models
 
 *  *sphere*
@@ -28,10 +28,10 @@
 *  *parallelepiped*
 
-In general, the scattering length density (SLD, = |beta|) in each region where the
+In general, the scattering length density (SLD, = $\beta$) in each region where the
 SLD is uniform, is a combination of the nuclear and magnetic SLDs and, for polarised
 neutrons, also depends on the spin states of the neutrons.
 
-For magnetic scattering, only the magnetization component, *M*\ :sub:`perp`,
-perpendicular to the scattering vector *Q* contributes to the the magnetic
+For magnetic scattering, only the magnetization component, $M_\perp$,
+perpendicular to the scattering vector $Q$ contributes to the the magnetic
 scattering length.
 
@@ -42,6 +42,6 @@
 .. image:: dm_eq.png
 
-where |gamma| = -1.913 is the gyromagnetic ratio, |mu|\ :sub:`B` is the
-Bohr magneton, *r*\ :sub:`0` is the classical radius of electron, and |sigma|
+where $\gamma = -1.913$ is the gyromagnetic ratio, $\mu_B$ is the
+Bohr magneton, $r_0$ is the classical radius of electron, and $\sigma$
 is the Pauli spin.
 
@@ -55,8 +55,8 @@
 .. image:: M_angles_pic.png
 
-If the angles of the *Q* vector and the spin-axis (*x'*) to the *x*-axis are |phi|
-and |theta|\ :sub:`up`, respectively, then, depending on the spin state of the
+If the angles of the $Q$ vector and the spin-axis (*x'*) to the *x*-axis are $\phi$
+and $\theta_\text{up}$, respectively, then, depending on the spin state of the
 neutrons, the scattering length densities, including the nuclear scattering
-length density (|beta|\ :sub:`N`) are
+length density ($\beta_N$) are
 
 .. image:: sld1.png
@@ -78,6 +78,6 @@
 .. image:: mqy.png
 
-Here, *M*\ :sub:`0x`, *M*\ :sub:`0y` and *M*\ :sub:`0z` are the x, y and z components
-of the magnetization vector given in the laboratory xyz frame given by
+Here, $M_{0x}$, $M_{0y}$ and $M_{0z}$ are the $x$, $y$ and $z$ components
+of the magnetization vector given in the laboratory $xyz$ frame given by
 
 .. image:: m0x_eq.png
@@ -87,5 +87,5 @@
 .. image:: m0z_eq.png
 
-and the magnetization angles |theta|\ :sub:`M` and |phi|\ :sub:`M` are defined in
+and the magnetization angles $\theta_M$ and $\phi_M$ are defined in
 the figure above.
 
@@ -93,8 +93,8 @@
 
 ===========   ================================================================
- M0_sld        = *D*\ :sub:`M` *M*\ :sub:`0`
- Up_theta      = |theta|\ :sub:`up`
- M_theta       = |theta|\ :sub:`M`
- M_phi         = |phi|\ :sub:`M`
+ M0_sld        = $D_M M_0$
+ Up_theta      = $\theta_\text{up}$
+ M_theta       = $\theta_M$
+ M_phi         = $\phi_M$
  Up_frac_i     = (spin up)/(spin up + spin down) neutrons *before* the sample
  Up_frac_f     = (spin up)/(spin up + spin down) neutrons *after* the sample
Index: src/sas/sasgui/perspectives/fitting/media/pd_help.rst
===================================================================
--- src/sas/sasgui/perspectives/fitting/media/pd_help.rst	(revision 6aad2e8b144e92be94b299c8ae7f4f418c094f1d)
+++ src/sas/sasgui/perspectives/fitting/media/pd_help.rst	(revision 5ed76f8390eac56ff5d3779fd9c454d0c8fe1dc0)
@@ -24,8 +24,10 @@
 form factor is normalized by the average particle volume such that
 
-*P(q) = scale* * \ <F*\F> / *V + bkg*
+.. math::
 
-where F is the scattering amplitude and the \<\> denote an average over the size
-distribution.
+    P(q) = \text{scale} \langle F^*F rangle V + \text{background}
+
+where $F$ is the scattering amplitude and $\langle\cdot\rangle$ denotes an average
+over the size distribution.
 
 Users should note that this computation is very intensive. Applying polydispersion
@@ -57,8 +59,8 @@
 .. image:: pd_image001.png
 
-where *xmean* is the mean of the distribution, *w* is the half-width, and *Norm* is a
-normalization factor which is determined during the numerical calculation.
+where $x_{mean}$ is the mean of the distribution, $w$ is the half-width, and $Norm$
+is a normalization factor which is determined during the numerical calculation.
 
-Note that the standard deviation and the half width *w* are different!
+Note that the standard deviation and the half width $w$ are different!
 
 The standard deviation is
@@ -81,5 +83,5 @@
 .. image:: pd_image005.png
 
-where *xmean* is the mean of the distribution and *Norm* is a normalization factor
+where $x_{mean}$ is the mean of the distribution and $Norm$ is a normalization factor
 which is determined during the numerical calculation.
 
@@ -100,6 +102,6 @@
 .. image:: pd_image007.png
 
-where |mu|\ =ln(*xmed*), *xmed* is the median value of the distribution, and
-*Norm* is a normalization factor which will be determined during the numerical
+where $\mu=\ln(x_{med})$, $x_{med}$ is the median value of the distribution, and
+$Norm$ is a normalization factor which will be determined during the numerical
 calculation.
 
@@ -107,5 +109,5 @@
 size parameter in the *FitPage*, for example, radius = 60.
 
-The polydispersity is given by |sigma|
+The polydispersity is given by $\sigma$
 
 .. image:: pd_image008.png
@@ -115,10 +117,10 @@
 .. image:: pd_image009.png
 
-The mean value is given by *xmean*\ =exp(|mu|\ +p\ :sup:`2`\ /2). The peak value
-is given by *xpeak*\ =exp(|mu|-p\ :sup:`2`\ ).
+The mean value is given by $x_{mean} =\exp(\mu + p^2 /2)$. The peak value
+is given by $x_{peak} =\exp(\mu-p^2)$.
 
 .. image:: pd_image010.jpg
 
-This distribution function spreads more, and the peak shifts to the left, as *p*
+This distribution function spreads more, and the peak shifts to the left, as $p$
 increases, requiring higher values of Nsigmas and Npts.
 
@@ -132,9 +134,11 @@
 .. image:: pd_image011.png
 
-where *xmean* is the mean of the distribution and *Norm* is a normalization factor
-which is determined during the numerical calculation, and *z* is a measure of the
+where $x_{mean}$ is the mean of the distribution and $Norm$ is a normalization factor
+which is determined during the numerical calculation, and $z$ is a measure of the
 width of the distribution such that
 
-z = (1-p\ :sup:`2`\ ) / p\ :sup:`2`
+.. math::
+
+    z = (1-p^2 ) / p^2
 
 The polydispersity is
@@ -156,5 +160,5 @@
 
 This user-definable distribution should be given as as a simple ASCII text file
-where the array is defined by two columns of numbers: *x* and *f(x)*. The *f(x)*
+where the array is defined by two columns of numbers: $x$ and $f(x)$. The $f(x)$
 will be normalized by SasView during the computation.
 
@@ -172,5 +176,5 @@
 
 SasView only uses these array values during the computation, therefore any mean
-value of the parameter represented by *x* present in the *FitPage*
+value of the parameter represented by $x$ present in the *FitPage*
 will be ignored.
 
@@ -181,5 +185,5 @@
 
 Many commercial Dynamic Light Scattering (DLS) instruments produce a size
-polydispersity parameter, sometimes even given the symbol *p*! This parameter is
+polydispersity parameter, sometimes even given the symbol $p$! This parameter is
 defined as the relative standard deviation coefficient of variation of the size
 distribution and is NOT the same as the polydispersity parameters in the Lognormal
Index: src/sas/sasgui/perspectives/fitting/media/residuals_help.rst
===================================================================
--- src/sas/sasgui/perspectives/fitting/media/residuals_help.rst	(revision 780545820bf596bf3709c434f3eb26bcc29a1236)
+++ src/sas/sasgui/perspectives/fitting/media/residuals_help.rst	(revision 5ed76f8390eac56ff5d3779fd9c454d0c8fe1dc0)
@@ -18,5 +18,5 @@
 also provides two other measures of the quality of a fit:
 
-*  |chi|\  :sup:`2` (or 'Chi2'; pronounced 'chi-squared')
+*  $\chi^2$ (or 'Chi2'; pronounced 'chi-squared')
 *  *Residuals*
 
@@ -32,15 +32,17 @@
 *Npts* such that
 
-  *Chi2/Npts* = { SUM[(*Y_i* - *Y_theory_i*)^2 / (*Y_error_i*)^2] } / *Npts*
+.. math::
 
-This differs slightly from what is sometimes called the 'reduced chi-squared'
+  \chi^2/N_{pts} =  \sum[(Y_i - Y_{theory}_i)^2 / (Y_error_i)^2] } / N_{pts}
+
+This differs slightly from what is sometimes called the 'reduced $\chi^2$'
 because it does not take into account the number of fitting parameters (to
-calculate the number of 'degrees of freedom'), but the 'normalized chi-squared'
-and the 'reduced chi-squared' are very close to each other when *Npts* >> number of
-parameters.
+calculate the number of 'degrees of freedom'), but the 'normalized $\chi^2$
+and the 'reduced $\chi^2$ are very close to each other when $N_{pts} \gg
+\text{number of parameters}.
 
-For a good fit, *Chi2/Npts* tends to 0.
+For a good fit, $\chi^2/N_{pts}$ tends to 1.
 
-*Chi2/Npts* is sometimes referred to as the 'goodness-of-fit' parameter.
+$\chi^2/N_{pts}$ is sometimes referred to as the 'goodness-of-fit' parameter.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
@@ -53,10 +55,12 @@
 value and its *true* value is its error).
 
-*SasView* calculates 'normalized residuals', *R_i*, for each data point in the
+*SasView* calculates 'normalized residuals', $R_i$, for each data point in the
 fit:
 
-  *R_i* = (*Y_i* - *Y_theory_i*) / (*Y_err_i*)
+.. math::
 
-For a good fit, *R_i* ~ 0.
+  R_i = (Y_i - Y_theory_i) / (Y_err_i)
+
+For a good fit, $R_i \sim 0$.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
Index: src/sas/sasgui/perspectives/fitting/media/sm_help.rst
===================================================================
--- src/sas/sasgui/perspectives/fitting/media/sm_help.rst	(revision 6aad2e8b144e92be94b299c8ae7f4f418c094f1d)
+++ src/sas/sasgui/perspectives/fitting/media/sm_help.rst	(revision 5ed76f8390eac56ff5d3779fd9c454d0c8fe1dc0)
@@ -20,14 +20,14 @@
 ==================
 
-Sometimes the instrumental geometry used to acquire the experimental data has 
-an impact on the clarity of features in the reduced scattering curve. For 
-example, peaks or fringes might be slightly broadened. This is known as 
-*Q resolution smearing*. To compensate for this effect one can either try and 
-remove the resolution contribution - a process called *desmearing* - or add the 
-resolution contribution into a model calculation/simulation (which by definition 
-will be exact) to make it more representative of what has been measured 
+Sometimes the instrumental geometry used to acquire the experimental data has
+an impact on the clarity of features in the reduced scattering curve. For
+example, peaks or fringes might be slightly broadened. This is known as
+*Q resolution smearing*. To compensate for this effect one can either try and
+remove the resolution contribution - a process called *desmearing* - or add the
+resolution contribution into a model calculation/simulation (which by definition
+will be exact) to make it more representative of what has been measured
 experimentally - a process called *smearing*. SasView will do the latter.
 
-Both smearing and desmearing rely on functions to describe the resolution 
+Both smearing and desmearing rely on functions to describe the resolution
 effect. SasView provides three smearing algorithms:
 
@@ -36,5 +36,5 @@
 *  *2D Smearing*
 
-SasView also has an option to use Q resolution data (estimated at the time of 
+SasView also has an option to use $Q$ resolution data (estimated at the time of
 data reduction) supplied in a reduced data file: the *Use dQ data* radio button.
 
@@ -43,6 +43,6 @@
 dQ Smearing
 -----------
- 
-If this option is checked, SasView will assume that the supplied dQ values 
+
+If this option is checked, SasView will assume that the supplied $dQ$ values
 represent the standard deviations of Gaussian functions.
 
@@ -65,7 +65,7 @@
 **[Equation 1]**
 
-The functions |inlineimage004| and |inlineimage005|
-refer to the slit width weighting function and the slit height weighting 
-determined at the given *q* point, respectively. It is assumed that the weighting
+The functions $W_v(v)$ and $W_u(u)$
+refer to the slit width weighting function and the slit height weighting
+determined at the given $q$ point, respectively. It is assumed that the weighting
 function is described by a rectangular function, such that
 
@@ -80,8 +80,9 @@
 **[Equation 3]**
 
-so that |inlineimage008| |inlineimage009| for |inlineimage010| and *u*\ .
-
-Here |inlineimage011| and |inlineimage012| stand for
-the slit height (FWHM/2) and the slit width (FWHM/2) in *q* space.
+so that $\Delta q_\alpha = \int_0^\infty d\alpha W_\alpha(\alpha)$
+for $\alpha = v$ and $u$.
+
+Here $\Delta q_u$ and $\Delta q_v$ stand for
+the slit height (FWHM/2) and the slit width (FWHM/2) in $q$ space.
 
 This simplifies the integral in Equation 1 to
@@ -91,15 +92,16 @@
 **[Equation 4]**
 
-which may be solved numerically, depending on the nature of |inlineimage011| and |inlineimage012| .
+which may be solved numerically, depending on the nature of
+$\Delta q_u$ and $\Delta q_v$.
 
 Solution 1
 ^^^^^^^^^^
 
-**For** |inlineimage012| **= 0 and** |inlineimage011| **= constant.**
+**For $\Delta q_v= 0$ and $\Delta q_u = \text{constant}$.**
 
 .. image:: sm_image016.png
 
-For discrete *q* values, at the *q* values of the data points and at the *q*
-values extended up to *q*\ :sub:`N`\ = *q*\ :sub:`i` + |inlineimage011| the smeared
+For discrete $q$ values, at the $q$ values of the data points and at the $q$
+values extended up to $q_N = q_i + \Delta q_u$ the smeared
 intensity can be approximately calculated as
 
@@ -108,23 +110,23 @@
 **[Equation 5]**
 
-where |inlineimage018| = 0 for *I*\ :sub:`s` when *j* < *i* or *j* > *N-1*.
+where |inlineimage018| = 0 for $I_s$ when $j < i$ or $j > N-1$.
 
 Solution 2
 ^^^^^^^^^^
 
-**For** |inlineimage012| **= constant and** |inlineimage011| **= 0.**
+**For $\Delta q_v = \text{constant}$ and $\Delta q_u= 0$.**
 
 Similar to Case 1
 
-|inlineimage019| for *q*\ :sub:`p` = *q*\ :sub:`i` - |inlineimage012| and *q*\ :sub:`N` = *q*\ :sub:`i` + |inlineimage012|
+|inlineimage019| for $q_p = q_i - \Delta q_v$ and $q_N = q_i + \Delta q_v$
 
 **[Equation 6]**
 
-where |inlineimage018| = 0 for *I*\ :sub:`s` when *j* < *p* or *j* > *N-1*.
+where |inlineimage018| = 0 for $I_s$ when $j < p$ or $j > N-1$.
 
 Solution 3
 ^^^^^^^^^^
 
-**For** |inlineimage011| **= constant and** |inlineimage011| **= constant.**
+**For $\Delta q_u = \text{constant}$ and $\Delta q_v = \text{constant}$.**
 
 In this case, the best way is to perform the integration of Equation 1
@@ -142,7 +144,6 @@
 **[Equation 7]**
 
-for *q*\ :sub:`p` = *q*\ :sub:`i` - |inlineimage012| and *q*\ :sub:`N` = *q*\ :sub:`i` + |inlineimage012|
-
-where |inlineimage018| = 0 for *I*\ :sub:`s` when *j* < *p* or *j* > *N-1*.
+for *q_p = q_i - \Delta q_v$ and $q_N = q_i + \Delta q_v$
+where |inlineimage018| = 0 for *I_s$ when $j < p$ or $j > N-1$.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
@@ -175,16 +176,17 @@
 **[Equation 9]**
 
-In Equation 9, *x*\ :sub:`0` = *q* cos(|theta|), *y*\ :sub:`0` = *q* sin(|theta|), and
-the primed axes, are all in the coordinate rotated by an angle |theta| about
-the z-axis (see the figure below) so that *x'*\ :sub:`0` = *x*\ :sub:`0` cos(|theta|) +
-*y*\ :sub:`0` sin(|theta|) and *y'*\ :sub:`0` = -*x*\ :sub:`0` sin(|theta|) +
-*y*\ :sub:`0` cos(|theta|). Note that the rotation angle is zero for a x-y symmetric
-elliptical Gaussian distribution. The *A* is a normalization factor.
+In Equation 9, $x_0 = q \cos(\theta)$, $y_0 = q \sin(\theta)$, and
+the primed axes, are all in the coordinate rotated by an angle $\theta$ about
+the z-axis (see the figure below) so that
+$x'_0 = x_0 \cos(\theta) + y_0 \sin(\theta)$ and
+$y'_0 = -x_0 \sin(\theta) + y_0 \cos(\theta)$.
+Note that the rotation angle is zero for a $xy$ symmetric
+elliptical Gaussian distribution. The $A$ is a normalization factor.
 
 .. image:: sm_image023.png
 
-Now we consider a numerical integration where each of the bins in |theta| and *R* are
-*evenly* (this is to simplify the equation below) distributed by |bigdelta|\ |theta|
-and |bigdelta|\ R, respectively, and it is further assumed that *I(x',y')* is constant
+Now we consider a numerical integration where each of the bins in $\theta$ and $R$ are
+*evenly* (this is to simplify the equation below) distributed by $\Delta \theta$
+and $\Delta R$, respectively, and it is further assumed that $I(x',y')$ is constant
 within the bins. Then
 
@@ -194,6 +196,6 @@
 
 Since the weighting factor on each of the bins is known, it is convenient to
-transform *x'-y'* back to *x-y* coordinates (by rotating it by -|theta| around the
-*z* axis).
+transform $x'y'$ back to $xy$ coordinates (by rotating it by $-\theta$ around the
+$z$ axis).
 
 Then, for a polar symmetric smear
@@ -207,5 +209,5 @@
 .. image:: sm_image026.png
 
-while for a *x-y* symmetric smear
+while for a $xy$ symmetric smear
 
 .. image:: sm_image027.png
@@ -225,9 +227,9 @@
 -------------------------
 
-In all the cases above, the weighting matrix *W* is calculated on the first call
-to a smearing function, and includes ~60 *q* values (finely and evenly binned)
-below (>0) and above the *q* range of data in order to smear all data points for
-a given model and slit/pinhole size. The *Norm*  factor is found numerically with the
-weighting matrix and applied on the computation of *I*\ :sub:`s`.
+In all the cases above, the weighting matrix $W$ is calculated on the first call
+to a smearing function, and includes ~60 $q$ values (finely and evenly binned)
+below (>0) and above the $q$ range of data in order to smear all data points for
+a given model and slit/pinhole size. The $Norm$  factor is found numerically with the
+weighting matrix and applied on the computation of $I_s$.
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
