# Towards Reliable Neural Generative Modeling of Detectors

arXiv: https://arxiv.org/abs/2204.09947 · HTML: https://mlsanity.com/paper/2204.09947/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Generative Models for Fast Calorimeter Simulation.LHCb case](https://mlsanity.com/paper/1812.01319/) (2018)
- [Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks](https://mlsanity.com/paper/2012.04595/) (2020)

## Built on by (most cited first, 1 of 1)

- [The LHCb ultra-fast simulation option, Lamarr: design and validation](https://mlsanity.com/paper/2309.13213/) (2023)

## Abstract (arXiv)

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.