Implicit quantile networks for emulation in jet physics
Abstract The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task in domains outside physics. In this work, we illustrate the potential of IQNs as components of emulators using the simulation of jets as an example. Specifically, we use an IQN to map jets described by their 4-momenta at the generation level to jets at the event reconstruction level. The conditional densities emulated by our model closely match those generated by Delphes , while also enabling faster jet simulation.
Authors
- Raghuram Ramanujan (ORCID: https://orcid.org/0009-0008-3476-3379)
- Ali Al Kadhim (ORCID: https://orcid.org/0000-0003-3490-8407)
- Michelle Kuchera (ORCID: https://orcid.org/0000-0002-9735-6147)
- Braden Kronheim (ORCID: https://orcid.org/0000-0003-0704-0972)
- H. Prosper (ORCID: https://orcid.org/0000-0002-4077-2713)
Institutions
- Florida State University (US)
- Davidson College (US)
- University of Maryland, College Park (US)
Publication Details
- Journal
- Machine Learning Science and Technology
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1088/2632-2153/ad9884
- Primary Topic
- Gaussian Processes and Bayesian Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- U.S. Department of Energy