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Background properties in experimental particle physics are typicallyestimated using large data sets. However, different events can exhibitdifferent features because of the quantum mechanical nature of the underlyingphysics processes. While signal and background fractions in a given data setcan be evaluated using a maximum likelihood estimator, the shapes of thecorresponding distributions are traditionally obtained using high-statisticscontrol samples, which normally neglects the effect of fluctuations. On theother hand, if it was possible to subtract background using templates that takefluctuations into account, this would be expected to improve the resolution ofthe observables of interest, and to reduce systematics depending on theanalysis. This study is an initial step in this direction. We propose a novelalgorithm inspired by the Gibbs sampler that makes it possible to estimate theshapes of signal and background probability density functions from a givencollection of particles, using control sample templates as initial conditionsand refining them to take into account the effect of fluctuations. Results onMonte Carlo data are presented, and the prospects for future development arediscussed.

A sampling algorithm to estimate the effect of fluctuations in particle physics data