Stitching Monte Carlo samples

TL;DR

This paper introduces a method called 'stitching' that combines overlapping Monte Carlo samples in high-energy physics by applying weights, enabling more efficient use of computational resources for large-scale simulations.

Contribution

The paper presents a novel weighting procedure for combining overlapping MC samples, improving simulation efficiency in high-energy physics analyses.

Findings

Effective method for combining overlapping MC samples

Reduces computational resources needed for large datasets

Applicable to proton-proton collision simulations at LHC

Abstract

Monte Carlo (MC) simulations are extensively used for various purposes in modern high-energy physics (HEP) experiments. Precision measurements of established Standard Model processes or searches for new physics often require the collection of vast amounts of data. It is often difficult to produce MC samples containing an adequate number of events to allow for a meaningful comparison with the data, as substantial computing resources are required to produce and store such samples. One solution often employed when producing MC samples for HEP experiments is to partition the phase space of particle interactions into multiple regions and produce the MC samples separately for each region. This approach allows to adapt the size of the MC samples to the needs of physics analyses that are performed in these regions. In this paper we present a procedure for combining MC samples that overlap in phase space. The procedure is based on applying suitably chosen weights to the simulated events. We refer to the procedure as "stitching". The paper includes different examples for applying the procedure to simulated proton-proton collisions at the CERN Large Hadron Collider.