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Abstract

The estimation of parameters from data is a common problem in many areas of the physical sciences, and frequently used algorithms rely on sets of simulated data which are fit to data. In this article, an analytic solution for simulation-based parameter estimation problems is presented. The matrix formalism, termed the Linear Template Fit, calculates the best estimators for the parameters of interest. It combines a linear regression with the method of least squares. The algorithm uses only predictions calculated for a few values of the parameters of interest, which have been made available prior to its execution. The Linear Template Fit is particularly suited for performance-critical applications and parameter estimation problems with computationally intense simulations, which are otherwise often limited in their usability for statistical inference. Equations for error propagation are discussed in detail and are given in closed analytic form. For the solution of problems with a nonlinear dependence on the parameters of interest, the Quadratic Template Fit is introduced. As an example application, a determination of the strong coupling constant from inclusive jet cross section data at the CERN Large Hadron Collider is studied and compared with previously published results.