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Physics Simulations

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Abstract and 1. Introduction

  1. Unfolding

    2.1 Posing the Unfolding Problem

    2.2 Our Unfolding Approach

  2. Denoising Diffusion Probabilistic Models

    3.1 Conditional DDPM

  3. Unfolding with cDDPMs

  4. Results

    5.1 Toy models

    5.2 Physics Results

  5. 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

B Physics Simulations


Table 2: List of physics simulations generated for the data-driven detector simulation, along with the corresponding parton distribution functions (PDFs), parton shower models, phase space biases, and their inclusion in the training dataset. Simulations not included in the training dataset were used as test datasets.


Authors:

(1) Camila Pazos, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts;

(2) Shuchin Aeron, Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions;

(3) Pierre-Hugues Beauchemin, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions;

(4) Vincent Croft, Leiden Institute for Advanced Computer Science LIACS, Leiden University, The Netherlands;

(5) Martin Klassen, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts;

(6) Taritree Wongjirad, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions.


This paper is available on arxiv under CC BY 4.0 DEED license.


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