Efficient binned profile likelihood minimization for precision measurements with RABBIT
This paper introduces RABBIT, a Python framework leveraging TensorFlow 2's differentiable programming and just-in-time compilation to enable fast, robust, and scalable binned profile likelihood minimization for precision LHC measurements, outperforming existing tools in high-dimensional regimes.
Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the high-energy physics experiments at the Large Hadron Collider, scientists do not simply look for a single particle appearing in a detector. Instead, they must reconstruct the story of billions of collisions by sorting the debris into thousands of tiny categories, or bins, based on energy and other properties. To make sense of this mountain of data, researchers build complex statistical models that predict what the data should look like if their theories are correct. These models must account for the inevitable "noise" of the experiment, such as slight variations in the detector's performance or the environment, which are treated as adjustable knobs called nuisance parameters. The goal is to turn the model's knobs until the prediction matches the observed data as closely as possible, a process known as a fit. As the collider gathers more data, the number of bins and the number of these adjustment knobs grow into the thousands, making the mathematical task of finding the perfect match increasingly difficult and time-consuming for existing computer tools.
To solve this growing bottleneck, a team of researchers has developed a new software framework called RABBIT, which stands for Rapid Automatic Bin-Based Inference Tool. This system is designed to handle the massive scale of modern particle physics measurements, specifically those involving the High-Luminosity LHC, where the volume of data will be far greater than what current machines produce. The researchers built RABBIT using a modern programming approach that treats the entire statistical model as a single, continuous flow of calculations. This allows the computer to calculate exactly how the final result changes when any single knob is turned, a capability that older methods often approximate or struggle to compute quickly. By combining this precise calculation with techniques that allow the software to run efficiently on both standard computer processors and specialized graphics chips, the team created a tool that can process models with hundreds of thousands of bins and thousands of variables in a fraction of the time required by established tools.
The researchers tested RABBIT using synthetic models that mimic the complexity of real particle physics experiments, generating fake data with known properties to see how well the software could recover the correct answers. In these tests, they compared RABBIT against the standard tools currently used by the physics community. When the models were simple, all the tools performed similarly, finishing the calculations in seconds. However, as the complexity increased to include 100,000 bins and over 1,000 adjustment knobs, the older tools began to struggle, often failing to find a solution within a reasonable time frame or running out of memory. In contrast, RABBIT continued to work smoothly, completing fits that took the other tools over ten minutes in under one minute, and successfully solving models with 100,000 bins that caused the other software to fail entirely. The software achieved this speed by using a specific mathematical strategy that avoids calculating the full, massive matrix of relationships between every single variable, instead focusing only on the most direct paths to the solution.
Beyond raw speed, the new framework offers a more flexible way to handle the data itself. It allows scientists to define physical quantities, such as the cross-section of a particle interaction, as a direct transformation of the model's output. This means that once the model is fitted to the data, the software can automatically calculate the final physical result and its uncertainty without needing to re-run the entire complex fitting process. The researchers also demonstrated that the tool can handle different ways of treating systematic errors, including methods that simplify the math by treating variations as symmetric or additive, which further speeds up the process without sacrificing accuracy. The team validated their results by showing that RABBIT produces the exact same statistical values as the current standard software when both are applied to the same model, confirming that the new speed does not come at the cost of correctness.
The implications of this work are significant for the future of particle physics. The High-Luminosity LHC will generate data sets so large that the current generation of analysis tools may not be able to process them efficiently, potentially limiting the precision of future measurements. RABBIT provides a pathway to analyze these massive datasets, enabling scientists to measure physical observables with unprecedented detail. The framework has already been applied to real-world measurements, such as the determination of the W boson mass, and the authors suggest that its ability to scale to millions of data points will be essential for fully exploiting the data from the collider and beyond. By making the statistical analysis of complex, high-dimensional data faster and more robust, this tool ensures that the scientific community can continue to extract the most precise possible answers from the most challenging experiments.
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