Machine Learning in High Energy Physics Community White Paper (1807.02876v3)

Abstract: Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. We detail a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

• The paper presents ML strategies for advancing particle physics research through optimized simulation, real-time analysis, and object reconstruction.
• It details the application of generative models, deep neural networks, and boosted decision trees to efficiently handle high-volume, complex data.
• The document emphasizes collaborative initiatives, specialized hardware investments, and robust uncertainty quantification for future experimental breakthroughs.

Overview of Machine Learning in High Energy Physics Community White Paper

The "Machine Learning in High Energy Physics Community White Paper" outlines a comprehensive strategy for integrating ML advancements into particle physics research, particularly in context with the High-Luminosity Large Hadron Collider (HL-LHC) and upcoming neutrino experiments. The document reflects extensive collaboration across the high-energy physics (HEP) community, data science experts, and industry partners, which seeks to address upcoming computational challenges with modern ML techniques.

Historical Context and Importance

Machine learning applications in particle physics have significantly evolved since the 1990s. Initial use-cases involved high-level physics analysis, progressively moving into particle and event identification and reconstruction. The prominence of ML has surged, driven by sophisticated algorithms and increased computing power. This progression is crucial for efficiently handling data from HL-LHC, which is expected to provide an integrated luminosity 20 times larger than previous datasets, challenging present computational methods in managing event size and dataset complexity.

Identified Research Areas and Challenges

The paper details several ML-driven research areas, presenting a multi-faceted approach to advancing particle physics:

1. Simulation: Fast generative models, such as Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs), promise substantial improvements over traditional Monte Carlo simulations. Although these models provide rapid generation capabilities, ensuring their accuracy to an acceptable level remains challenging.
2. Real-Time Analysis and Triggering: ML methods are pivotal in offsetting computational costs associated with real-time data processing crucial for identifying rare events amidst high collision frequencies at the LHC. Techniques involving boosted decision trees have shown potential in facilitating swift inference for large datasets.
3. Object Reconstruction and Identification: Deep neural networks (DNNs) are explored for enhancing feature extraction from detector outputs, with particular emphasis on applications like calorimeters and time projection chambers (TPCs). These advancements are essential for refining the precision of high-dimensional pattern recognition tasks.
4. Application in the Matrix Element Method: The integration of ML techniques aims to streamline the computational demands of the Matrix Element Method, heralding more efficient parameter estimations and enhancing precision physics measurements.
5. Uncertainty Quantification: Properly assigning uncertainty to ML outputs remains a challenge, however, is crucial for robust applications within HEP. Bayesian approaches and other statistical methods are highlighted as potential pathways to address this issue.

Collaborative Initiatives and Future Directions

The document emphasizes the importance of collaboration between the ML and HEP communities. It encourages the formation of common benchmark datasets, joint workshops, and challenges to foster innovation and align scientific understanding and objectives across disciplines. The need for cross-disciplinary endeavors points to the necessity of educational programs targeting ML competencies within the physics community.

Computational and Resource Requirements

Implementing ML-driven methodologies demands significant investment in specialized hardware, such as GPUs, FPGAs, and potentially emergent technologies like TPUs for reducing analysis latency. Developing a systemic use of High-Performance Computing (HPC) infrastructure appears essential for training complex models effectively.

Conclusion

This white paper underscores machine learning’s pivotal role in addressing foreseeable scientific inquiries and computational hurdles within high-energy physics. By setting forth a detailed roadmap, the document aims to elucidate a clear path and initiate fruitful collaborations spanning academia, industry, and the broader data science domain. These efforts aspire to extend the boundaries of ML applicability, leveraging it as a linchpin in advancing the capabilities and reach of experimental particle physics research.