A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network

Abstract

Highly reliable Monte-Carlo event generators and detector simulation programs are important for the precision measurement in the high energy physics. Huge amounts of computing resources are required to produce a sufficient number of simulated events. Moreover, simulation parameters have to be fine-tuned to reproduce situations in the high-energy particle interactions which is not trivial in some phase spaces in physics interests. In this paper, we suggest a new method based on the Wasserstein Generative Adversarial Network (WGAN) that can learn the probability distribution of the real data. Our method is capable of event generation at a very short computing time compared to the traditional MC generators. The trained WGAN is able to reproduce the shape of the real data with high fidelity.

Appendices

A. Normalized distributions of momentum components

See Figs. 5, 6 and 7.

Fig. 5

The distributions of three momentum components $(p_x, p_y, p_z)$ of the subleading photon

Fig. 6

The distributions of three momentum components $(p_x, p_y, p_z)$ of the leading b-jet

Fig. 7

The distributions of three momentum components $(p_x, p_y, p_z)$ of the subleading b-jet

B. Normalized distributions of additional variables

See Figs. 8, 9, 10 and 11.

Fig. 8

The distributions of $p_{\text {T}}$ and $\eta$ of the subleading photon

Fig. 9

The distributions of $p_{\text {T}}$ and $\eta$ of the leading b-jet

Fig. 10

The distributions of $p_{\text {T}}$ and $\eta$ of the subleading b-jet

Fig. 11

The distributions of invariant mass of two b-jets and $\Delta R (b\text {-jet}_1, b\text {-jet}_2$)

C. Normalized distributions of the control region

See Fig. 12.

Fig. 12

The distributions of the invariant mass of two photons and $\Delta R (\gamma _{1}, \gamma _{2})$ with the condition of [100, 150] GeV