Overview
Particle physics experiments are anticipated to encounter significant data acquisition challenges, encompassing high data rates, large volumes, extreme operating environments, and operational constraints. Addressing these challenges necessitates the implementation of methodologies for real-time inference, decision-making, intelligent data reduction, and processing architectures that exceed current capabilities. This white paper articulates a community-driven vision to identify and prioritize research and development opportunities at the intersection of emerging technologies and particle physics applications.
Research Context
The success of the forthcoming experimental paradigm in particle physics is dependent on the adoption of several emerging technologies. These include artificial intelligence and machine learning (AI/ML), advances in silicon microelectronics, and the development of quantum algorithms and processing. The confluence of these technologies defines areas of research relevant to particle physics data handling, such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, and novel codesign and synthesis strategies. Additionally, the scope extends to readout mechanisms for cryogenic or high-radiation environments, and analog computing. The overarching goal is to facilitate a transition to a new data frontier in fundamental science by addressing the requirements for hardware-based ML systems and their corresponding physics applications.
Approach
The approach outlined in the white paper involves a community-driven process to identify and prioritize specific research and development opportunities. This prioritization is centered on hardware-based ML systems that can be applied to particle physics. The objective is to contribute to a successful adaptation to the anticipated data landscape of future fundamental science experiments, which are characterized by unprecedented data rates, volumes, environmental extremes, and operational limitations.
Findings
- Future particle physics experiments will face challenges related to data acquisition due to unprecedented data rates and volumes.
- These experiments will also operate under extreme environments and operational constraints.
- Harnessing this data for scientific discovery requires real-time inference and decision-making capabilities.
- Intelligent data reduction techniques are essential for managing the expected data volumes.
- Efficient processing architectures beyond current capabilities are necessary.
- Emerging technologies identified as crucial include artificial intelligence and machine learning (AI/ML), silicon microelectronics, and quantum algorithms and processing.
- Research areas at the intersection of these technologies include low-power and low-latency devices for edge computing.
- Heterogeneous accelerator systems represent another area of research focus.
- Reconfigurable hardware is considered an important development area.
- Novel codesign and synthesis strategies are identified as relevant.
- Readout technologies suitable for cryogenic or high-radiation environments are also part of this intersection.
- Analog computing is another identified area of research.
- A community-driven vision is presented for identifying and prioritizing research and development opportunities in hardware-based ML systems and their corresponding physics applications.
Why This Matters
The ability to manage and extract scientific insights from the vast and complex data generated by next-generation particle physics experiments hinges on advancements in data acquisition and processing. The integration of AI/ML with specialized hardware, including edge computing and quantum systems, is deemed critical for enabling real-time decision-making and data reduction necessary for scientific discovery under challenging experimental conditions.