Abstract
We present a new way of performing hypothesis tests on scattering data, by means of a perturbatively calculable classifier. This classifier exploits the “history tree” of how the measured data point might have evolved out of any simpler (reconstructed) points along classical paths, while explicitly keeping quantum–mechanical interference effects by copiously employing complete leading-order matrix elements. This approach extends the standard Matrix Element Method to an arbitrary number of final state objects and to exclusive final states where reconstructed objects can be collinear or soft. We have implemented this method into the standalone package hytrees and have applied it to Higgs boson production in association with two jets, with subsequent decay into photons. hytrees allows to construct an optimal classifier to discriminate this process from large Standard Model backgrounds. It further allows to find the most sensitive kinematic regions that contribute to the classification.
Highlights
This approach underlies the socalled Matrix Element Method (MEM) [2], which has been used in a large variety of contexts [3,4,5,6,7,8,9]
In [10,11] the parton-level MEM has been extended to including the parton shower in the evaluation of the probabilities, and has been implemented in Shower [10,11,12] and Event [13,14,15] Deconstruction, thereby allowing for the analysis of an arbitrary number of final state objects
Double-counting between jets generated during the parton shower step or at the matrix element level is avoided by explicit vetoes and the inclusion of Sudakov factors or
Summary
[16]) to produce pseudo-data of scattering events In this context, several frameworks to combine the parton shower with multiple hard matrix elements for multi-jet processes have been laid out [17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37].
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