Standard Model EFT

One way of parameterizing new physics beyond the Standard Model in a model-agnostic way is to use Effective Field Theory techniques in the so-called “Standard Model Effective Field Theory”, or SMEFT. In the SMEFT, the new physics is treated using higher-dimension operators of Standard Model fields, which lead to deviations in both the coupling strength and the kinematics of interactions between Standard Model particles which one can hope to measure at colliders. The most effective way of measuring the effects of these new operators is by measuring not only the rates of different rare processes at the LHC, but also the kinematic distributions in these processes.

One direction in my research is to understand how best to make these differential measurements. To do this, we use machine learning techniques, implemented in the package MadMiner to compute quantities closely related to the “Optimal Observables” which have been used in various applications for years. These observables help us understand the best limits we can obtain on theory parameters given the data. This approach also has close ties to what’s known as “Information Geometry” — which quantifies this statistical information in an elegant way. Our first application of these methods was to the associated W-Higgs production mode, where we used our information geometry approach to evaluate the utility of the so-called “Simplified Template Cross Sections”. In the future we hope to extend this approach to other gauge/Higgs processes, and gain more insight into the connections between Information Geometry, Machine Learning, and their applications to studying the SMEFT.

An orthogonal direction is to understand the effects of higher-order QCD corrections in the case of the SMEFT. While there has been tremendous progress in making predictions for Standard Model processes with greater and greater accuracy, usually, only the lowest-order calculations are used for beyond the Standard Model predictions. While this is sometimes fine as a first approximation, there are some cases where computing higher order corrections in the presence of SMEFT effects is important as well. In a recent paper we demonstrated that higher-order effects are extremely important in WZ production, and dramatically change the limits obtained from LHC data. In the future, we’ll continue to extend this approach to other processes, and try to understand the consequences in a global fit to gauge/Higgs data.

Samuel Homiller
Post-Doctoral Fellow

Post-Doctoral Fellow in the HETG at Harvard University.


We examine the importance of one-loop matching of the singlet onto the SM Effective Field Theory, quantifying the effects via a global …

We examine SMEFT fits for common extensions of the SM to understand how global fits should be interpreted in the context of new …

We extend our analysis of NLO corrections in the SMEFT on $WW$ and $WZ$ to include the $WH$ and $ZH$ processes.

We show that NLO effects in the SMEFT have dramatic effects on the observed limits obtained from $WW$ and $WZ$ production data.

We analyze WH production using the MadMiner machine learning tool and quantify the information in kinematic phase space.


An investigation of the WH production process in the Standard Model Effective Field Theory using the Machine Learning tool MadMiner.