Related papers: Exploring SMEFT in VH with Machine Learning
We present current bounds on SMEFT operators that are mainly constrained by Higgs-boson observables, under different assumptions for the flavour structure of the UV theory. We investigate how the accuracy reached through a dedicated…
We perform a parameter fit in the Standard Model Effective Field Theory (SMEFT) with an emphasis on using regularized linear regression to tackle the issue of the large number of parameters in the SMEFT. In regularized linear regression a…
The Standard Model Effective Field Theory (SMEFT) is the parametrization chosen to interpret many modern measurements. We have recently discussed, building on the work of other groups, that its overall framework can be experimentally…
Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics…
Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in…
We obtain SMEFT bounds using an approach that utilises the complete multi-dimensional differential information of a process. This approach is based on the fact that at a given EFT order, the full angular distribution in the most important…
The study of Higgs boson production at large transverse momentum is one of the new frontiers for the LHC Higgs physics programme. This paper considers boosted Higgs production in the Standard Model Effective Field Theory (SMEFT). We focus…
Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories…
We study Higgs boson production via vector boson fusion at the LHC, focusing on the process $pp \to H + jj$ and capturing the leading energy-enhanced contributions within the Standard Model Effective Field Theory (SMEFT) up to order…
Extracting bounds on BSM operators at hadron colliders can be a highly non-trivial task. It can be useful or, depending on the complexity of the event structure, even essential to employ modern analysis techniques in order to measure…
We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two different models,…
In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning…
Beginning from a basic neural-network architecture, we test the potential benefits offered by a range of advanced techniques for machine learning, in particular deep learning, in the context of a typical classification problem encountered…
Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset.…
We study the measurement of Higgs boson self-couplings through $2\rightarrow 3$ vector boson scattering (VBS) processes in the framework of Standard Model effective field theory (SMEFT) at both proton and lepton colliders. The SMEFT…
We present a momentum-dependent reweighting strategy to extend current LHC di-Higgs analyses within the $\kappa$-framework and SMEFT into the bosonic sector of the Higgs Effective Field Theory (HEFT). Unlike SMEFT, where symmetry…
In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron…
We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters…
The Simplified Template Cross Section (STXS) program has become the standard interface between Higgs measurements and global fits, but its fixed one-dimensional boundaries are not guaranteed to align with the phase-space directions to which…
We study the Higgs couplings involved in the $Zh$ associated production mode at the Large Hadron Collider (LHC) in presence of Higgs-gauge boson coupling modifiers via $\kappa$ framework, and dimension 6 Standard Model Effective Theory…