spinw.fitspec method

fits experimental spin wave data

Syntax

fitsp = fitspec(obj,Name,Value)

Description

fitsp = fitspec(obj,Name,Value) uses a heuristic method to fit spin wave spectrum using a few simple rules to define the goodness (or R-value) of the fit:

All calculated spin wave modes that are outside of the measured energy range will be omitted.
Spin wave modes that are closer to each other than the given energy bin will be binned together and considered as one mode in the fit.
If the number of calculated spin wave modes after applying rule 1&2 is larger than the observed number, the weakes simulated modes will be removed from the fit.
If the number of observed spin wave modes is larger than the observed number, fake spin wave modes are added with energy equal to the limits of the scan; at the upper or lower limit depending on which is closer to the observed spin wave mode.

After these rules the number of observed and simulated spin wave modes will be equal. The R-value is defined as:

$R = \sqrt{ \frac{1}{n_E} \cdot \sum_{i,q} \frac{1}{\sigma_{i,q}^2}\left(E_{i,q}^{sim} - E_{i,q}^{meas}\right)^2},$

where $(i,q)$ indexing the spin wave mode and momentum respectively. $E_{sim}$ and $E_{meas}$ are the simulated and measured spin wave energies, sigma is the standard deviation of the measured spin wave energy determined previously by fitting the inelastic peak. $n_E$ is the number of energies to fit.

The R value is optimized using particle swarm algorithm to find the global minimum.

Name-Value Pair Arguments

'func'

Function to change the Hamiltonian in obj, it needs to have the following header:

obj = @func(obj, x)

'datapath'

Path to the file that stores the experimental data. For the input data format see sw_readspec.

'Evect'

Column vector with $n_E$ elements that defines the energy binning of the calculated dispersion. Larger binning steps solve the issue of fitting unresolved modes.

'xmin'

Lower limit of the optimisation parameters, optional.

'xmax'

Upper limit of the optimisation parameters, optional.

'x0'

Starting value of the optimisation parameters. If empty or undefined, random values are used within the given limits.

'optimizer'

String that determines the type of optimizer to use, possible values:

'pso' Particle-swarm optimizer, see ndbase.pso, default.
'simplex' Matlab built-in simplex optimizer, see fminsearch.

'nRun'

Number of consecutive fitting runs, each result is saved in the output fitsp.x and fitsp.R arrays. If the Hamiltonian given by the random x parameters is incompatible with the ground state, those x values will be omitted and new random x values will be generated instead. Default value is 1.

'nMax'

Maximum number of runs, including the ones that produce error (due to incompatible ground state). Default value is 1000.

'hermit'

Method for matrix diagonalization, for details see spinw.spinwave.

'epsilon'

Small number that controls wether the magnetic structure is incommensurate or commensurate, default value is $10^{-5}$ .

'imagChk'

Checks that the imaginary part of the spin wave dispersion is smaller than the energy bin size. Default is true.

Parameters for visualizing the fit results:

'plot': If true, the measured dispersion is plotted together with the fit. Default is true.
'iFact': Factor of the plotted simulated spin wave intensity (red ellipsoids).
'lShift': Vertical shift of the Q point labels on the plot.

Optimizer options:

'TolX': Minimum change of x when convergence reached, default value is $10^{-4}$ .
'TolFun': Minimum change of the R value when convergence reached, default value is $10^{-5}$ .
'MaxFunEvals': Maximum number of function evaluations, default value is $10^7$ .
'MaxIter': Maximum number of iterations for the [ndbse.pso] optimizer. Default value is 20.

Output Arguments

Output fitsp is struct type with the following fields:

obj Copy of the input obj, with the best fitted Hamiltonian parameters.
x Final values of the fitted parameters, dimensions are $[n_{run}\times n_{par}]$ . The rows of x are sorted according to increasing R values.
redX2 Reduced $\chi^2_\eta$ value, goodness of the fit stored in a column vector with $n_{run}$ number of elements, sorted in increasing order. $\chi^2_\eta$ is defined as:

$\begin{align} \chi^2_\eta &= \frac{\chi^2}{\eta},\\ \eta &= n-m+1, \end{align}$ where η is the degree of freedom, $n$ number of observations and $m$ is the number of fitted parameters.
exitflag Exit flag of the fminsearch command.
output Output of the fminsearch command.

Note: As a rule of thumb when the variance of the measurement error is known a priori, χ $^2_\eta$ ≫ 1 indicates a poor model fit. A χ $^2_\eta$ ≫ 1 indicates that the fit has not fully captured the data (or that the error variance has been underestimated). In principle, a value of χ $^2_\eta$ = 1 indicates that the extent of the match between observations and estimates is in accord with the error variance. A χ $^2_\eta$ < 1 indicates that the model is ‘over-fitting’ the data: either the model is improperly fitting noise, or the error variance has been overestimated.

Any other option used by spinw.spinwave function are also accepted.

spinw.fitspec method

Syntax

Description

Name-Value Pair Arguments

Output Arguments

See Also