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Due to the large size of datasets accumulated at the LHC, analysis results are often limited by systematic effects. The application of multivariate analysis techniques such as Boosted Decision Trees (BDTs) or artificial neural nets typically maximises the statistical significance of the results while ignoring systematic effects. There is a known strategy to mitigate systematic effects for neural nets but no firmly established procedure for BDTs. We present a method to incorporate systematic uncertainties into a BDT, the systematics-aware BDT (saBDT). We evaluate our method on open data of the ATLAS Higgs machine learning challenge and compare our results to neural nets trained with an adversary.

Incorporation of systematic uncertainties in the training of multivariate methods