Preserving physically important variables in optimal event selections: A case study in Higgs physics
Jul 3, 2019Citations per year
Abstract: (arXiv)
Analyses of collider data, often assisted by modern Machine Learning methods, condense a number of observables into a few powerful discriminants for the separation of the targeted signal process from the contributing backgrounds. These discriminants are highly correlated with important physical observables; using them in the event selection thus leads to the distortion of physically relevant distributions. We present a novel method based on a differentiable estimate of mutual information, a measure of non-linear dependency between variables, to construct a discriminant that is statistically independent of a number of selected observables, and so manages to preserve their distributions in the event selection. Our strategy is evaluated in a realistic setting, the analysis of the Standard Model Higgs boson decaying into a pair of bottom quarks. Using the distribution of the invariant mass of the di-b-jet system to extract the Higgs boson signal strength, our method achieves state-of-the-art performance compared to other decorrelation techniques, while significantly improving the sensitivity of a similar, cut-based, analysis published by ATLAS.- Hadron-Hadron scattering (experiments)
- Higgs physics
- Particle and resonance production
- Higgs particle: decay
- Higgs particle --> bottom bottom
- sensitivity
- background
- numerical methods
- data analysis method
References(40)
Figures(13)
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