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Simulation-based inference in particle physics

Nature Reviews Physics volume 3, page 305 (2021)Cite this article

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To turn the vast amount of data collected in particle collider experiments into precise measurements of nature, particle physicists have to find subtle patterns in high-dimensional data. To this end, particle physicists use complex computer simulations which encode our understanding of elementary particle collisions, additional radiation patterns, particle decays, hadronization, interactions with the detector, and sensor readout. Unfortunately, the analysis of particle-collision data with computer simulations often faces a fundamental challenge.

Over the years, different methods for parameter inference have been developed that do not require a tractable likelihood function. These methods are collectively known as simulation-based, or likelihood-free, inference techniques. Historically, the most common approach has been to reduce both observed and simulated high-dimensional data to a single kinematic variable. The likelihood function in this one-dimensional summary statistic can then be estimated with histograms, enabling both frequentist and Bayesian inference. But the reduction to a single kinematic variable almost invariably discards useful information from the original data, and parameter measurements constructed in this way will then be less precise than an analysis of the full high-dimensional data could be. The histogram method does not scale to high-dimensional data as it would require an exorbitantly large number of simulations to be run.

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Acknowledgements

This publication was created by Johann Brehmer while working at New York University. He is currently an engineer at Qualcomm Technologies Netherlands B.V.

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  1. Center for Data Science, New York University, New York, United States

    Johann Brehmer

  2. Qualcomm Technologies Netherlands B.V., Amsterdam, Netherlands

    Johann Brehmer

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  1. Johann Brehmer
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Correspondence to Johann Brehmer.

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Brehmer, J. Simulation-based inference in particle physics. Nat Rev Phys 3, 305 (2021). https://doi.org/10.1038/s42254-021-00305-6

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