Related papers: Exploring the SMEFT landscape: Bayesian Model Sele…
It is not only conceivable but likely that the spectrum of physics beyond the Standard Model (SM) is non-degenerate. The lightest non-SM particle may reside close enough to the electroweak scale that it can be kinematically probed at…
We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of effective resistance, we derive an EM-type algorithm…
We present the Standard Model Effective Field Theories (SMEFT) from purely on-shell arguments. Starting from a few basic assumptions such as Poincar\'e invariance and locality, we classify all the renormalisable and non-renormalisable…
The Standard Model effective field theory (SMEFT) provides a general framework to include the dynamics of the beyond standard model physics residing at a certain higher energy scale $\Lambda$. We study the top-quark production along with a…
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.…
Effective field theory is an effective approach to parameterizing effects of high energy scale physics in low energy measurements. The two popular frameworks for physics beyond the standard model are the so-called standard model effective…
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…
This paper presents recent methodological advances to perform simulation-based inference (SBI) of a general class of Bayesian hierarchical models (BHMs), while checking for model misspecification. Our approach is based on a two-step…
We present techniques for estimating the effects of systematic uncertainties in unbinned data analyses at the LHC. Our primary focus is constraining the Wilson coefficients in the standard model effective field theory (SMEFT), but the…
Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in estimation and uncertainty quantification. For example, in…
At energies below the electroweak scale, baryon number $B$ and lepton number $L$ violating processes are of significant importance in identifying the nature of UV extensions of the Standard Model. The imprint of UV theories on low-energy…
Even if some experimental evidence suggests the existence of physics beyond the SM, no clues of new resonances can be found in the data. In the case their masses are much larger than the energies of current experiments, the SMEFT formalism…
The Standard Model Effective Field Theory (SMEFT) provides a robust framework for probing deviations in the couplings of Standard Model particles from their theoretical predictions. This framework relies on an expansion in…
This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by the structure of the governing equations, the task of learning a reduced-order model from data is posed as a Bayesian…
A set of constructs, definitions, and propositions that present a systematic view of the Standard Model Effective Field Theory (SMEFT), i.e. how the influence of higher energy processes is localizable in a few structural properties which…
Symbolic regression is a powerful tool for discovering governing equations directly from data, but its sensitivity to noise hinders its broader application. This paper introduces a Sequential Monte Carlo (SMC) framework for Bayesian…
We illustrate how Bayesian reweighting can be used to incorporate the constraints provided by new measurements into a global Monte Carlo analysis of the Standard Model Effective Field Theory (SMEFT). This method, extensively applied to…
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are…
Projecting measurements of the interactions of the known Standard Model (SM) states into an effective field theory (EFT) framework is an important goal of the LHC physics program. The interpretation of measurements of the properties of the…
We write down a geometric realization of the Standard Model Effective Field Theory (SMEFT) extended by $n_f$ flavours of light sterile neutrinos, a so-called geo$\nu$SMEFT. As with the geoSMEFT introduced by Helset, Martin and Trott, we…