Machine Learning in LHCb Simulation Achieves 0.01% Error with Two-Order-of-Magnitude Speedup

High-energy physics relies heavily on detailed computer simulations to interpret experimental data, but these simulations are becoming increasingly demanding of computational resources. Researchers at the National Centre for Nuclear Research, led by Michał Mazurek and Andrzeja Sołtana, are tackling this challenge with innovative machine learning techniques within the LHCb experiment. Their work introduces two approaches, CaloML and Lamarr, that dramatically accelerate the simulation of particle interactions within the detector’s calorimeter. These methods achieve speed-ups of up to two orders of magnitude while maintaining exceptional precision, with systematic errors on reconstructed energies as low as 0. 01 percent, promising to unlock new possibilities for data analysis and discovery.

Fast simulations and flash simulations, powered by machine learning techniques, offer promising solutions to this challenge, differing in their level of detail and speed. The CaloML framework accelerates the simulation of electromagnetic showers from 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 the CPU time of the entire simulation phase by two orders of magnitude compared to traditional Geant4-based methods. This paper presents these two approaches, detailing their methodologies, performance, and validation results.

Machine Learning Accelerates LHCb Simulation Performance

This document details the advancements in simulation techniques within the LHCb experiment, focusing on the integration of machine learning (ML) to accelerate and improve the process. Here’s a summary of the key points: 1. The Need for Faster Simulation: * Traditional Geant4-based simulations are computationally expensive, hindering efficient data processing and analysis, especially with the increased data rates of LHC Run 3. 2. Gauss and Gaussino: The Core Framework: * LHCb utilizes the Gauss simulation application and its core framework, Gaussino, which is designed to be experiment-agnostic and adaptable to new technologies0.

1. Fast Simulation Approaches: * CaloML (Fast Calorimeter Simulation): This approach uses generative models to replace detailed calorimeter simulations with faster, ML-based predictions of energy deposits. It achieves up to two orders of magnitude speedup while maintaining good accuracy. * Lamarr (Ultra-Fast Simulation): This is an even faster approach that directly parameterizes the high-level detector response using modular ML models. It achieves two orders of magnitude CPU reduction compared to Geant4, with the Pythia event generator becoming the main computational resource0.

2. Key Features and Technologies: * Generative Models: Variational Autoencoders, Generative Adversarial Networks, and Diffusion models are employed to learn and reproduce complex shower profiles in the calorimeter. * Modular Design: Lamarr’s modularity allows for independent development and improvement of individual components, including tracking, particle identification, and calorimeter responses. * Gauss-on-Gaussino: The integration of Gauss and Gaussino provides a flexible and efficient platform for incorporating new simulation technologies0. 5.

Validation and Performance: * Both CaloML and Lamarr have been extensively validated against full Geant4 simulations, demonstrating good agreement in reconstructed observables. * Performance metrics show significant speedups with minimal impact on physics performance0. 6. Future Directions: * Exploring more sophisticated generative models to further enhance realism and precision. * Expanding the modularity and adaptability of the simulation framework. * Making these technologies available to the broader High Energy Physics (HEP) community. In essence, the document highlights LHCb’s successful implementation of ML-based fast simulation techniques to address the computational challenges of modern particle physics experiments, paving the way for more efficient data analysis and physics discovery.

Fast Simulation for LHCb Data Analysis

The research team has developed and validated two innovative approaches to accelerate simulations crucial for high-energy physics data analysis within the LHCb experiment. CaloML utilizes machine learning, employing generative models, to simulate electromagnetic showers of electrons and photons in the calorimeter, achieving simulation speeds up to one hundred times faster than traditional methods while maintaining a remarkably low systematic error of 0. 01% on reconstructed energies. Validation studies demonstrate excellent agreement between CaloML and detailed simulations, ensuring the fidelity of reconstructed events.

Complementing this, the team also created Lamarr, a flash simulation framework that parameterizes the overall detector response using modular machine learning models for tracking, particle identification, and calorimeter responses. Lamarr achieves a two-order-of-magnitude reduction in CPU time compared to conventional Geant4-based simulations, with the primary computational demand shifting to the Pythia event generator. While both methods demonstrate substantial speed improvements, the authors acknowledge current limitations in accurately modeling neutral particle interactions and particle-to-particle correlations within Lamarr. Future work focuses on addressing these challenges through the implementation of advanced architectures, with plans to integrate Lamarr into the broader LHCb simulation framework and potentially make it available to the wider high-energy physics community.