Speaker
Description
Searches for new particles often span a wide mass range, where both signal and SM background shapes vary significantly. We introduce a multivariate method that fully exploits the correlation between signal and background features and the explored mass scale. The classifiers—either a neural network or boosted decision tree—produce continuous outputs across the full mass range, achieving performance similar to classifiers trained for the specific mass.
The key advantages arise from two factors:
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The background mass scale is correlated with the actual background shape, enabling more effective background identification across all mass scales.
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A balanced training sample that spans the entire mass range, allowing the classifier to learn the differences between high and low scales.
We benchmark this approach with single production of a vector-like quark singlet T at the HL-LHC, where the cross section depends on both the mixing angle and the quark mass. Our method is effective for mass-unspecific searches, applicable to a wide range of new physics processes and collider settings. Mass-unspecific classifiers show strong performance, especially in searches spanning a broad mass range.