
A Guide to Constraining Effective Field Theories with Machine Learning
We develop, discuss, and compare several inference techniques to constra...
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MadMiner: Machine learningbased inference for particle physics
The legacy measurements of the LHC will require analyzing highdimension...
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Effective LHC measurements with matrix elements and machine learning
One major challenge for the legacy measurements at the LHC is that the l...
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Constraining Effective Field Theories with Machine Learning
We present powerful new analysis techniques to constrain effective field...
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SimulationBased Inference with Approximately Correct Parameters via Maximum Entropy
Inferring the input parameters of simulators from observations is a cruc...
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AI Safety for High Energy Physics
The field of highenergy physics (HEP), along with many scientific disci...
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Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
Data analysis in science, e.g., highenergy particle physics, is often s...
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Simulationbased inference methods for particle physics
Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate highfidelty simulated data, but they are not wellsuited for inference on the theory parameters with observed data. We explain why the likelihood function of highdimensional LHC data cannot be explicitly evaluated, why this matters for data analysis, and reframe what the field has traditionally done to circumvent this problem. We then review new simulationbased inference methods that let us directly analyze highdimensional data by combining machine learning techniques and information from the simulator. Initial studies indicate that these techniques have the potential to substantially improve the precision of LHC measurements. Finally, we discuss probabilistic programming, an emerging paradigm that lets us extend inference to the latent process of the simulator.
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