Machine Learning–Driven Strategies for Effective Pileup Suppression

Abstract

Pileup is a critical challenge in modern high-energy physics experiments, particularly at high-luminosity colliders where multiple particle interactions occur simultaneously within a single detector readout. These overlapping events introduce significant noise and ambiguity, complicating the accurate reconstruction of physics objects such as jets, missing transverse energy, and vertex positions. Traditional pileup mitigation techniques rely on rule-based algorithms and handcrafted features derived from detector geometry and event-level statistics. While effective to a degree, these approaches often struggle to scale with increasing collision rates and detector complexity. Recent advances in machine learning (ML) have opened new avenues for addressing pileup by enabling data-driven, adaptive, and highly granular mitigation strategies. Machine learning models, including supervised learning algorithms, deep neural networks, and graph-based architectures, can learn complex, non-linear correlations between detector signals and underlying physical interactions. By leveraging low-level detector information, such as tracking data and calorimeter deposits, ML-based methods can more precisely distinguish between particles originating from the primary interaction and those produced by secondary pileup events. This abstract explores the application of machine learning techniques to pileup suppression, highlighting their advantages over conventional methods in terms of accuracy, robustness, and scalability. It also discusses the integration of ML models into real-time and offline data processing pipelines, along with challenges related to training data quality, model interpretability, and computational efficiency. Overall, machine learning–driven pileup mitigation represents a transformative approach that enhances signal purity and enables more precise measurements in next-generation particle physics experiments.