Saturday 15 March 2025
Scientists have been searching for a smoking gun signal of beyond-the-standard-model physics at the Large Hadron Collider, but so far, none has been found. Instead, they’ve been using data from various experiments to constrain new physics domains and predict rates of experimental observables. This process is becoming increasingly challenging due to the growing precision of data and the dimensionality of parameter spaces.
A team of researchers has proposed a novel approach to tackle this problem by combining nested sampling with normalizing flows. Nested sampling is a technique used to estimate complex integrals, such as those appearing in Bayesian inference. Normalizing flows, on the other hand, are machine learning models that transform random variables into new ones while preserving their probability distributions.
The researchers applied this combination to the Type-II Seesaw model, a theoretical framework that attempts to explain the matter-antimatter asymmetry of the universe. They used data from experiments like the Large Electron Positron Collider and the Fermilab Tevatron to constrain the model’s parameters.
One of the key challenges in this approach is efficiently exploring the vast parameter space. The researchers employed a nested sampling algorithm that iteratively refines an initial proposal distribution using a likelihood function. This allows them to focus on regions of the parameter space with high posterior probabilities.
The normalizing flow component was used to transform the complex, multi-dimensional probability distributions into simpler ones. This enabled the researchers to use standard machine learning techniques to sample from the transformed distributions and obtain approximate samples from the original posterior distribution.
The results show that this combined approach can efficiently explore large parameter spaces and provide accurate estimates of posterior distributions. The researchers were able to constrain the Type-II Seesaw model’s parameters using a wide range of experimental data, including those from Higgs boson production and neutrino oscillations.
This technique has far-reaching implications for particle physics and beyond. By combining machine learning and Bayesian inference, scientists can tackle complex problems in fields like cosmology, condensed matter physics, and even biology. As experimental precision continues to increase, this approach will become increasingly important for making sense of the vast amounts of data being generated.
In practical terms, this method allows physicists to better understand the behavior of particles at high energies and may help them identify new physics beyond the standard model. It also has the potential to be applied to other areas of science where complex data analysis is required, such as in medical imaging or climate modeling.
Cite this article: “Combining Machine Learning and Bayesian Inference to Constrain New Physics Domains”, The Science Archive, 2025.
Machine Learning, Bayesian Inference, Large Hadron Collider, Particle Physics, Beyond-Standard-Model Physics, Type-Ii Seesaw Model, Normalizing Flows, Nested Sampling, Parameter Space Exploration, Data Analysis.
