Abstract
We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element – parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space. We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques from statistics, machine learning, and combinatorial optimization. We also review some of the recent research in this direction that has been enabled with Ginkgo. We show how probabilistic programming can be used to efficiently sample the showering process, how a novel trellis algorithm can be used to efficiently marginalize over the enormous number of clustering histories for the same observed particles, and how the dynamic programming and reinforcement learning can be used to find the maximum likelihood clusterinng in this enormous search space. This work builds bridges with work in hierarchical clustering, statistics, combinatorial optmization, and reinforcement learning.
Highlights
Jets are the most copiously produced objects at physics colliders, such as the Large Hadron Collider (LHC) or the Relativistic Heavy Ion Collider (RHIC), and the subject of intense experimental and theoretical study
3.2 Hierarchical clustering through reinforcement learning we review results from [32] that cast hierarchical clustering as a Markov Decision Process (MDP) and adapted reinforcement learning algorithms to solve it
This paper introduces a framing of common tasks in jet physics in probabilistic terms
Summary
Jets are the most copiously produced objects at physics colliders, such as the Large Hadron Collider (LHC) or the Relativistic Heavy Ion Collider (RHIC), and the subject of intense experimental and theoretical study. We will discuss a few areas where such computational bottlenecks appear and identify emerging computational techniques that may be able to address them We hope that this may challenge some assumptions about the computational demands of simulation, reconstruction, and analysis of collider physics data when jets are involved. The hadronization and detector simulation fit in this framing as well, but we do not discuss it explicitly in this work We find it elucidating to reframe the following concepts in jet physics in probabilistic terms:. The joint likelihood corresponds to what is coded in PYTHIA [1], Herwig [2], and Sherpa [3], but often in terms of accept-reject sampling and procedural code that does not explicitly expose the probabilities themselves This motivates Ginkgo, which provides convenient access to these quantities in a simplified parton shower. We will review some of the recent research into these problems enabled with Ginkgo
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
