Enhancing searches for resonances with machine learning and moment decomposition

Abstract

A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures. Such structures could result in a false signal when the background is estimated from data using sideband methods. A variety of techniques have been developed to construct classifiers which are independent from the resonant feature (often a mass). Such strategies are sufficient to avoid localized structures, but are not necessary. We develop a new set of tools using a novel moment loss function (Moment Decomposition or MoDe) which relax the assumption of independence without creating structures in the background. By allowing classifiers to be more flexible, we enhance the sensitivity to new physics without compromising the fidelity of the background estimation.

A cautionary tale of decorrelating theory uncertainties

“…This is generalized to Moment Decorrelation (MoDE) in Ref. [24] to allow for a given dependence of f on m. Pythia and Herwig is reduced due to the decorrelation requirement, resulting in a smaller estimate of the uncertainty, which no longer covers nature. These diagrams are meant only to be intuitive illustrations…”

“…A variety of techniques have been proposed to render a classifier independent of a given feature [13][14][15][16][17][18][19][20][21][22][23][24]. This has become an essential tool for resonance searches, where thresholds on the classifier output must not sculpt bumps in a given spectrum so that the Standard Model background can be estimated using sideband fits.…”

A Cautionary Tale of Decorrelating Theory Uncertainties

“…A variety of techniques have been proposed to render a classifier independent of a given feature [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. This has become an essential tool for resonance searches, where thresholds on the classifier must not sculpt bumps in a given spectrum so that the Standard Model background can be estimated using sideband fits.…”

Meta-learning and data augmentation for mass-generalised jet taggers

“…Examples include Refs. [14][15][16][17][18] as well as mass-decorrelation methods [19][20][21][22][23][24][25][26][27]. A comprehensive recent study of the performance of both data and training augmentation techniques and their implications for decorrelation is Ref.…”