Machine learning in LHCb Simulation: From fast to flash

TL;DR

This paper presents machine learning-based fast and flash simulation frameworks for LHCb calorimeter, significantly reducing computational time while maintaining high accuracy, thereby improving efficiency in high-energy physics simulations.

Contribution

Introduction of CaloML and Lamarr frameworks that accelerate calorimeter simulations by up to two orders of magnitude with minimal systematic errors.

Findings

CaloML achieves up to 100x speedup with 0.01% energy reconstruction error.

Lamarr reduces CPU time of full simulation by 100x compared to Geant4.

Both methods validated with performance and accuracy assessments.

Abstract

Monte Carlo simulations are essential for physics analyses in high-energy physics, but their computational demands are continuously increasing. In LHCb, 90 % of computing resources are used for simulations, with the calorimeter simulation being the most computationally intensive part. Fast simulations and flash simulations, leveraging machine learning techniques, offer promising solutions to this challenge with different levels of detail and speed. The CaloML framework accelerates electromagnetic shower propagation of photons and electrons in the LHCb calorimeter by up to two orders of magnitude, achieving a systematic error on reconstructed energies as low as 0.01\%. Lamarr is an in-house flash simulation framework that reduces CPU time of the whole simulation phase by two orders of magnitude compared to traditional Geant4-based methods. In this paper, these two approaches are presented, highlighting their methodologies, performance, and validation results, as well as future development plans.