Introduction and analysis of a method for the investigation of QCD-like tree data

TL;DR

This paper introduces an iterative neural network-based method to infer decay probability distributions in QCD-like jet data, enabling analysis of particle decay properties from detector observations.

Contribution

It presents a novel iterative approach using neural networks to reconstruct decay distributions from detector data, addressing challenges in analyzing jet formation.

Findings

Successfully reproduces true decay distributions from simulated detector data

Demonstrates effectiveness of neural network classifier in iterative reconstruction

Provides a new tool for studying particle decays in high-energy physics

Abstract

The properties of decays that take place during jet formation cannot be easily deduced from the final distribution of particles in a detector. In this work, we first simulate a system of particles with well defined masses, decay channels, and decay probabilities. This presents the "true system" for which we want to reproduce the decay probability distributions. Assuming we only have the data that this system produces in the detector, we decided to employ an iterative method which uses a neural network as a classifier between events produced in the detector by the "true system" and some arbitrary "test system". In the end, we compare the distributions obtained with the iterative method to the "true" distributions.