# new_matrixfit: perform a factorising fit of a matrix of correlation... In hadron: Analysis Framework for Monte Carlo Simulation Data in Physics

## Description

Modernised and extended implementation of matrixfit

## Usage

 1 2 3 4 5 new_matrixfit(cf, t1, t2, parlist, sym.vec = rep(1, cf$nrObs), neg.vec = rep("cosh", cf$nrObs), useCov = FALSE, model = "single", boot.fit = TRUE, fit.method = "optim", autoproceed = FALSE, par.guess, every, higher_states = list(val = numeric(0), boot = matrix(nrow = 0, ncol = 0), ampl = numeric(0)), ...) 

## Arguments

 cf Object of class cf with cf_meta and cf_boot. t1 Integer, start time slice of fit range (inclusive). t2 Integer, end time slie of fit range (inclusive). parlist Numeric vector, list of parameters for the model function. sym.vec Integer, numeric or vectors thereof specifying the symmetry properties of the correlation functions stored in cf. See matrixfit for details. neg.vec Integer or integer vector of global signs, see matrixfit for details. useCov Boolean, specifies whether a correlated chi^2 fit should be performed. model String, specifies the type of model to be assumed for the correlator. See below for details. boot.fit Boolean, specifies if the fit should be bootstrapped. fit.method String, specifies which minimizer should be used. See matrixfit for details. autoproceed Boolean, if TRUE, specifies that if inversion of the covariance matrix fails, the function should proceed anyway assuming no correlation (diagonal covariance matrix). par.guess Numeric vector, initial values for the paramters, should be of the same length as parlist. every Integer, specifies a stride length by which the fit range should be sparsened, using just everyth time slice in the fit. higher_states List with elements val and boot. Only used in the n_particles fit model. The member val must have the central energy values for all the states that are to be fitted. The boot member will be a matrix that has the various states as columns and the corresponding bootstrap samples as rows. The length of val must be the column number of boot. The row number of boot must be the number of samples. ... Further parameters.

## Details

There are different fit models available. The models generally depend on one or multiple energies E and amplitudes p_i which for a general matrix are row- and column-amplitudes. The relative sign factor c \in \{-1, 0, +1\} depends on the chosen symmetry of the correlator. It is a plus for a “cosh” symmetry and a minus for a “sinh” symmetry. If the back propagating part is to be neglected (just “exp” model), it will be zero.

When the back propagating part is not taken into account, then the single, shifted and weighted model become the same except for changes in the amplitude.

• single: The default model for a single state correlator is

\frac{1}{2} p_i p_j (\exp(-p_1 t) \pm c \exp(-p_2 (T-t))) \,.

• shifted: If the correlator has been shifted (using takeTimeDiff.cf, then the following model is applicable:

p_i p_j (\exp(-p_1 (t+1/2)) \mp c \exp(-p_1 (T-(t+1/2)))) \,.

• weighted: Works similarly to the shifted model but includes the effect of the weight factor from removeTemporal.cf.

• pc: In case only a single principal correlator from a GEVP is to be fitted this model can be used. It implements

\exp(-p_1(t-t_0))(p_2 + (1-p_2)\exp(-p_3 (t-t_0))

with t_0 the reference timesclice of the GEVP. See bootstrap.gevp for details.

• two_amplitudes: Should there be a single state but different amplitudes in the forward and backwards part, the following method is applicable.

\frac{1}{2} (p_2 \exp(-p_1 t) \pm c p_3 \exp(p_1 t))

This only works with a single correlator at the moment.

• single_constant: Uses the single model and simply adds + p_3 to the model such that a constant offset can be fitted. In total the model is

\mathrm{single}(p_1, p_2) + p_3 \,.

• n_particles: A sum of single models with independent energies and amplitudes:

∑_{i = 1}^n \mathrm{single}(p_{2n-1}, p_{2n}) \,.

Use the higher_states parameter to restrict the thermal states with priors to stabilize the fit.

## Value

See bootstrap.nlsfit.

hadron documentation built on Jan. 13, 2021, 8 a.m.