Machine Learning Gains from Data Compression Technique
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Scientists are tackling the challenge of efficiently processing complex data from particle collisions, a crucial step for advancing physics-informed machine learning. Wasikul Islam from the Department of Physics at the University of Wisconsin, Madison, and Sergei Chekanov from the HEP Division at Argonne National Laboratory, have developed a new method, RMM-C46, to compress high-dimensional data representing particle-collision events into a more manageable and interpretable format.
This research is significant because the original data contains over a thousand values per event, hindering large-scale training of machine learning models and limiting its use with emerging quantum computing technologies. By preserving key physical characteristics within a reduced dataset, achieving over a ten-fold reduction in size, RMM-C46 not only maintains but often improves the performance of machine learning tasks applied to simulated proton-proton collisions, offering a scalable and efficient pathway for next-generation collider physics analyses. Imagine condensing a complex orchestral score into just a few key phrases, retaining all the essential musical information. That is the challenge tackled by this development. This distils the immense data from particle collisions into a form readily usable by advanced computing. This streamlined approach promises to accelerate discoveries at the world’s largest atom smashers. Scientists are developing new methods to analyse data from high-energy particle collisions, a task becoming ever more reliant on machine learning. Identifying subtle signals of new physics within the immense volume of data generated by experiments like theLarge Hadron Colliderpresents a considerable challenge. Unsupervised anomaly detection, a technique for finding unexpected patterns without prior knowledge of what to look for, is gaining traction as a model-agnostic search strategy. However, current machine learning approaches often struggle with the high dimensionality and complex structure of collision events. Representing these events in a way that preserves essential physical information while remaining computationally manageable is difficult. The rapidity, mass matrix (RMM), a structured matrix encoding correlations between particles, has proven effective but typically contains over 1287 values per event. Here, this size creates problems for both large-scale training of machine learning models and for application to emerging quantum computing platforms with limited qubit availability. One key characteristic of the RMM is its fixed size, maintained by padding with zero values when events contain fewer particles than the maximum possible, which can hinder anomaly detection as algorithms may misinterpret them as genuine data rather than missing information. Previous attempts to reduce dimensionality through autoencoders or particle-flow reconstruction have yielded promising results, but often lack a direct connection to the underlying physics of the collision. Now, researchers have introduced RMM-C46, a compact, physics-driven representation designed to compress the RMM while retaining its interpretability. For instance, these new formats are constructed using aggregated invariant mass, rapidity differences — transverse energy components, reducing the original RMM’s size by more than tenfold. Applied to simulated proton-proton collisions at an energy of 13.6 TeV, these representations not only match but, in some cases, and surpass the performance of the full RMM in both supervised and unsupervised learning tasks. Beyond performance, the compactness and physical transparency of RMM-C46 make it particularly well-suited for integration with near-term quantum machine learning architectures. Rapidity-mass matrix compression via 46 component partitioning At a dimensionality of 46, the RMM-C46 representation preserves the essential physics of the full rapidity-mass matrix while drastically reducing its size. In turn, this compressed format achieves a reduction of over an order of magnitude in the number of variables compared to the original 51×51 RMM. Which contained 2601 elements per event. Specifically, the RMM-C46 consolidates information into 46 distinct zones, each linked to a specific physical quantity or pairwise structure within the event. These zones encompass one global MET term, five transverse-energy terms (one for each object class: jets, b-jets, muons, electrons, and photons), five transverse-mass-like terms, five longitudinal/Lorentz-like terms, fifteen rapidity-difference zones, and fifteen invariant-mass zones. Calculations of these 46 components are derived from non-overlapping regions of the full RMM, forming a complete partition of the matrix entries used in its construction. For instance, the transverse energy for each object class is obtained by summing the diagonal entries within the corresponding block of the original RMM, while the longitudinal terms are derived from the first row and column of the matrix, representing the scaled transverse mass and longitudinal Lorentz factors. The 15 rapidity-difference zones and 15 invariant-mass zones capture the pairwise correlations between different object types. By aggregating physically correlated regions into well-defined scalar quantities, the RMM-C46 format establishes a practical foundation for scalable machine learning pipelines. In simulated proton-proton collisions at a centre-of-mass energy of 13.6 TeV, the performance of models trained on RMM-C46 matched or exceeded that of models trained on the full RMM in both supervised and unsupervised machine learning tasks. Inclusive ttbar events served as the dominant Standard Model background in these analyses, with WZ+jets used for cross-validation. Beyond improved computational efficiency, the RMM-C46 representation offers one of the first event representations explicitly designed for deployment on low-qubit or hybrid quantum, classical architectures. Compressing particle interaction data for enhanced machine learning and physical insight Rather than simply improving performance on existing tasks, this development addresses a fundamental bottleneck in applying machine learning to particle physics data. Across years, the sheer volume of information generated by collider experiments has outstripped the capacity of many analytical tools, and particularly those envisioned for near-future quantum computing platforms. At the same time, the rapidity mass matrix, a detailed record of particle interactions, captures vital physics but is computationally expensive to process. The new ‘RMM-C46’ representation doesn’t merely shrink the data. It distils it into a form that preserves key physical relationships while dramatically reducing its size. By retaining the underlying physical structure, RMM-C46 offers a level of interpretability often lost in ‘black box’ machine learning models. Once algorithms are trained on this compressed data, physicists can more readily understand why a particular event is flagged as interesting, aiding in the search for new phenomena. In turn, beyond immediate applications in collider analysis, this approach could prove valuable in other areas dealing with complex, high-dimensional datasets, such as materials science or astrophysics. A critical question remains regarding the generalisability of this compression technique. While Outcomes presented demonstrate success with simulated data, the performance on real-world data, inevitably containing imperfections and noise, needs careful evaluation. Also, the specific choice of 46 features, though effective, may require further optimisation for different physics processes. Meanwhile, the broader effort to develop physics-informed machine learning is likely to see increased focus on similar dimensionality reduction strategies, potentially combining RMM-C46 with other techniques like moment pooling or graph neural networks. At the same time, the real test will be integration into full analysis pipelines and deployment at experiments like theLarge Hadron Collider If successful, this could unlock new avenues for discovering subtle signals hidden within the vast data streams, and perhaps even accelerate the development of quantum algorithms tailored to particle physics problems. The reduction in computational burden also opens the door to more extensive simulations and refined theoretical predictions, creating a virtuous cycle of discovery. 👉 More information 🗞 Compact Representation of Particle-Collision Events for Physics-Informed Machine Learning 🧠 ArXiv: https://arxiv.org/abs/2602.17563 Tags: