What happens when plasma planes meet? – Spoiler: Baysi Magic

Links table

Abstract and 1. Introduction

2. Vaccine systems

3. Medium effects

4. Conclusion and references

1. Introduction

Jetscape is a normative framework based on tasks to simulate all aspects of heavy collisions in Ion [1]. We first relate to adjusting Jetscape using a new Hadronization Unit: Hybrid Hadronization [2, 3]. This method is first used to re -install Monte Carlo [4, 5] On partners after the shower stage, then the rest is determined with the Lund series model [6]. We use Bayesian analysis to adjust Jetscape with Hybrid Hadronization to CMS and Phenix data in the vicious Proton-Proton systems. Although Jetscape primarily aims to calculate heavy ion collision, a solid void foundation is needed.

Then we study the moderate effects on Hadronization by modeling how one plane takes a brick of Kuark Glon plasma. Hybrid Hadronization is unique among the Hadronization models of Shower Monte Carlos where it can take into account the medium effects on Hadronization by re -installing a part of the shower with thermal Partons, and by allowing the thermal parties to become part of the chains that connect them with shower bottles. It is expected that both the presence of the mediator and the flow of the middle will have clear effects on the final cases produced. The medium flow in the direction of jet and transverse must provide the influx effects on the most softened Hadron.

2. Vaccine systems

The Bayesian analysis process begins with the creation of a group starting from the design points within the space of the teacher provided by the previous domains for each teacher. Our previous distribution of parameters is supposed to be flat within the area of ​​parameters, and we use Latin hyper to create these points. It will be used to run Jetscape. A galactic emulator is used to create noteing materials between the design points in the teacher’s space. Then the notes produced by data are compared. The Markov Monte Carlo series determines new sets of points that improve the description. This process is repeated to the rapprochement, which gives us the rear distribution. The rear distributions of a group of our parameters are displayed in Figure 1 with connections between parameters pairs. Figure 2 shows notes for rear distribution.