New Algorithms Spot Rare Particle Collisions in Real Time

Scientists at SLAC National Accelerator Laboratory, led by Sagar Addepalli, have developed a quantum-inspired machine learning approach utilising tensor networks for real-time anomaly detection in high-energy physics experiments. Their research details the implementation of a spaced matrix product operator (SMPO) and a cascaded SMPO architecture, specifically designed for deployment on field programmable gate array (FPGA) hardware. This innovative methodology offers a pathway towards significantly increased computational efficiency and enables applications at the forefront of collider experiments, providing a crucial toolkit for identifying rare events that may signal physics beyond the Standard Model.

Spaced matrix product operators enable edge deployment of real-time anomaly detection

A factor of ten reduction in resource requirements for real-time anomaly detection has now been achieved, enabling deployment at the detector “edge”, a feat previously considered impractical due to computational limitations. Sagar Addepalli and colleagues, collaborating across multiple institutions, have centred this breakthrough on the spaced matrix product operator (SMPO), a quantum-inspired machine learning algorithm. The SMPO leverages the principles of tensor network decomposition, a technique originally developed in the field of quantum many-body physics, to efficiently represent and process high-dimensional data. This representation allows for a substantial reduction in the number of parameters required to model complex relationships within the data, thereby decreasing computational demands. Implementation is achieved on field programmable gate array (FPGA) hardware, chosen for its ability to be reconfigured and optimised for specific tasks. The cascaded SMPO architecture further enhances efficiency by allowing for hierarchical processing of data, affording greater flexibility in resource-constrained environments vital for high energy physics experiments.

Meeting the stringent latency demands required for trigger deployment, typically on the order of microseconds, the system can analyse data almost instantaneously at particle colliders. This capability allows for immediate filtering of events, discarding the vast majority of background noise and identifying potentially new phenomena that deviate from established physics. The ability to perform this filtering in real-time is critical, as it prevents data overload and ensures that rare, potentially groundbreaking events are not missed. FPGAs offer increased flexibility and efficiency in implementation compared to traditional CPUs and GPUs, and enable sensitivity to physics beyond current understandings of the Standard Model. The SMPO’s efficient data representation and the FPGA’s parallel processing capabilities combine to create a powerful system for real-time analysis. Further work will focus on expanding the validation process to include a wider range of potential signals, including those arising from more complex theoretical models. This includes testing against simulated datasets incorporating various background processes and signal strengths to assess the robustness and accuracy of the anomaly detection system.

Real-time data analysis using tensor networks enables on-site anomaly detection at particle

Bigger Picture: High energy physics is entering an era defined by data, with the Large Hadron Collider (LHC) and future colliders generating unprecedented volumes of information. The LHC currently produces over one petabyte of data per second, a rate that is expected to increase significantly with future upgrades and new colliders. Increasingly sophisticated analysis techniques are required to tease out signals of new particles and forces from this immense data stream. A key proof of principle has been established for deploying complex machine learning directly within collider experiments, offering a promising route to real-time anomaly detection and shifting processing closer to the experiment itself. This ‘at the edge’ processing paradigm is crucial for managing the data deluge and extracting meaningful insights in a timely manner.

Tensor networks provide a means to process data at the high rates produced by the Large Hadron Collider and future machines, reducing data transmission needs and allowing for real-time identification of unusual events potentially indicating new physics beyond our current understanding. The computational complexity of traditional machine learning algorithms often scales poorly with the dimensionality of the data, making them impractical for real-time analysis of collider events. Tensor networks, however, offer a more efficient representation of high-dimensional data, reducing the computational burden. Implementing a spaced matrix product operator (SMPO), alongside its cascaded architecture, on field programmable gate arrays has achieved a reduction in resource requirements, enabling ‘at the edge’ processing. This means data can be analysed immediately at the experiment itself, rather than being transferred for off-site computation, which is often limited by bandwidth and latency. These networks efficiently represent complex data using interconnected mathematical entities, tensors, reducing the computational burden of identifying rare events potentially indicative of new physics. The SMPO specifically exploits the spatial correlations present in detector data, further enhancing its efficiency. This supports the acceleration of the search for physics beyond our current models, potentially enabling new discoveries soon. The ability to perform real-time analysis also opens up the possibility of adaptive data acquisition, where the detector can dynamically adjust its settings based on the incoming data, focusing on regions of interest and maximising the chances of discovering new phenomena. The development of this technology represents a significant step towards realising the full potential of future collider experiments and pushing the boundaries of our understanding of the universe.

Researchers successfully demonstrated real-time anomaly detection using a spaced matrix product operator (SMPO) implemented on field programmable gate array hardware. This is important because it allows for immediate analysis of data from particle colliders, overcoming limitations imposed by transferring large datasets for off-site processing. The cascaded SMPO architecture proved particularly efficient in representing complex data, potentially accelerating the search for new physics beyond the Standard Model. Future work could focus on deploying these quantum-inspired algorithms at larger experiments, enabling adaptive data acquisition and maximising the discovery potential of high-energy collisions.