# HYTREES: Combining Matrix Elements and Parton Shower for Hypothesis Testing

## TL;DR

HYTREES introduces a novel hypothesis testing method for scattering data that combines matrix elements and parton showers, enabling more precise discrimination of Higgs production signals from backgrounds.

## Contribution

The paper presents HYTREES, a new classifier that extends the Matrix Element Method by incorporating quantum interference and handling complex final states.

## Key findings

- HYTREES effectively discriminates Higgs signals from backgrounds.
- The method identifies the most sensitive kinematic regions.
- HYTREES is implemented as a standalone package.

## 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.