Mass conditioned and constrained normalising flows for jet generation

Journal of Physics Conference Series, vol. 3206, pp. 012068

Abstract

Abstract Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation with complex correlations between the points. In this study, the generation of jets with up to 30 constituents with Normalising Flows using Rational Quadratic Spline coupling layers is investigated. Without conditioning on the jet mass, our Normalising Flows are unable to model all correlations in data correctly, which is evident when comparing the invariant jet mass distributions between Monte Carlo simulated and model–generated data. Using the invariant mass as a condition for the coupling transformation enhances the performance on all tracked metrics. In addition, we demonstrate how to sample the original mass distribution by interpolating the empirical cumulative distribution function. Similarly, the variable number of constituents is taken care of by introducing an additional condition on the number of constituents in the jet. Furthermore, we study the usefulness of including an additional mass constraint in the loss term. On the JetNet dataset, our model yields a stable and quick training and performs comparably to the state-of-the-art.

Authors 6

1. Benno Käch

   Deutsches Elektronen-Synchrotron DESY

   Affiliation as printed

   Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85,

2. Dirk Krücker

   Deutsches Elektronen-Synchrotron DESY

   Affiliation as printed

   Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85,

3. Isabell-Alissandra Melzer-Pellmann

   Deutsches Elektronen-Synchrotron DESY

   Affiliation as printed

   Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85,

4. M. Scham Aachen

   Deutsches Elektronen-Synchrotron DESY · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85,

   Institute for Advanced Simulation - Jülich Supercomputing Centre

   RWTH Aachen University

5. S Schnake Aachen

   Deutsches Elektronen-Synchrotron DESY · RWTH Aachen University

   Affiliation as printed

   Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85,

   RWTH Aachen University

6. Alexi Verney-Provatas Aachen

   Deutsches Elektronen-Synchrotron DESY · RWTH Aachen University

   Affiliation as printed

   Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85,

   RWTH Aachen University