Physics Results

Table of Links

Abstract and 1. Introduction

2. Unfolding

   2.1 Posing the Unfolding Problem

   2.2 Our Unfolding Approach

3. Denoising Diffusion Probabilistic Models

   3.1 Conditional DDPM

4. Unfolding with cDDPMs

5. Results

   5.1 Toy models

   5.2 Physics Results

6. Discussion, Acknowledgments, and References

Appendices

A. Conditional DDPM Loss Derivation

B. Physics Simulations

C. Detector Simulation and Jet Matching

D. Toy Model Results

E. Complete Physics Results

5.2 Physics Results

We test our approach on particle physics data by applying it to jet datasets from various processes sampled using the PYTHIA event generator (details of these synthetic datasets can be found in appendix B). The generated truth-level jets were passed through two different detector simulation frameworks to simulate particle interactions within an LHC detector. The detector simulations used were DELPHES with the standard CMS configuration, and another detector simulator developed using an analytical data-driven approximation for the pT , η, and ϕ resolutions from results published by the ATLAS collaboration (more details in appendix C). The DELPHES CMS detector simulation is the standard and allows comparison to other machine-learning based unfolding algorithms, while the data-driven detector simulation tests the unfolding success under more drastic detector smearing.