Speaker
Description
I will present a search for a hypothetical heavy neutral gauge boson (Z′) decaying to dilepton pairs using machine-learning classifiers trained exclusively on low-mass kinematic sidebands. A gradient boosted decision tree (BDT) and a multi-layer perceptron deep neural network (DNN) are trained to distinguish Z-peak events from Drell-Yan background events using only 11 raw kinematic features, with dilepton invariant mass deliberately excluded to avoid background sculpting in the search region. The analysis uses proton-proton collision data with √s = 13 TeV recorded by the ATLAS experiment during LHC Run 2 in 2015, released through the CERN Open Data portal in 2024.
Both classifiers achieve strong discrimination on the held-out test set (AUC of 0.9869 and 0.9996 for the BDT and DNN respectively). Events in the blinded high-mass search region (mℓℓ > 250 GeV) are categorized by classifier score, a smooth three-parameter parametric function is used to model the background, and a bin-by-bin Asimov significance scan is performed. We observe no statistically significant excess, with the largest local deviation at mℓℓ = 1120 GeV at a significance of 2.40σ in the DNN's highest-score category.
The χ²/ndf ≈ 1.0 background fits confirm that the sideband-only training strategy preserves the background shape, demonstrating that ML classifiers can be used in bump-hunt searches without sculpting the spectrum. This methodology of concentrating signal-like events into high-score categories while preserving a smooth, fittable background is directly transferable to rare-event searches in neutrino physics and other BSM contexts. The analysis is fully reproducible and built entirely on public data.
| Contribution types | Short talk (15min + 5min Q/A) |
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