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A method for tuning parameters in Monte Carlo generators is described andapplied to a specific case. The method works in the following way: eachobservable is generated several times using different values of the parametersto be tuned. The output is then approximated by some analytic form to describethe dependence of the observables on the parameters. This approximation is usedto find the values of the parameter that give the best description of theexperimental data. This results in significantly faster fitting compared to anapproach in which the generator is called iteratively. As an application, weemploy this method to fit the parameters of the unintegrated gluon density usedin the CASCADE Monte Carlo generator, using inclusive deep inelastic datameasured by the H1 Collaboration. We discuss the results of the fit, itslimitations, and its strong points.

A method for tuning parameters of Monte Carlo generators and a its application to the determination of the unintegrated gluon density