Related papers: From Optimal Observables to Machine Learning: an E…
Precision phenomenological studies of high-multiplicity scattering processes at collider experiments present a substantial theoretical challenge and are vitally important ingredients in experimental measurements. Machine learning technology…
Future linear $e^+e^-$ colliders aim for extremely high precision measurements. To achieve this, not only excellent detectors and well controlled machine conditions are needed, but also the best possible estimate of backgrounds. To avoid…
We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy $ZW$ production with fully…
We consider the possibility of detecting vector resonances from a strong electroweak sector, in the framework of the BESS model, at future $e^+ e^-$ colliders up to the TeV range. If the mass $M_V$ of the new vector boson multiplet is not…
Simulation-based inference has been shown to outperform traditional, histogram-based inference in numerous phenomenological and experimental studies. So far, these analyses have focused on individual high-profile processes. We study the…
The main difficulty that arises in the analysis of most machine learning algorithms is to handle, analytically and numerically, a large number of interacting random variables. In this Ph.D manuscript, we revisit an approach based on the…
The next generation of $e^+e^-$ colliders will offer a possibility of clean testing of QCD dynamics. Recent progress in the theoretical description of exclusive processes permits for many of them a consistent use of the perturbative QCD…
New physics close to the electroweak scale is well motivated by a number of theoretical arguments. However, colliders, most notably the Large Hadron Collider (LHC), have failed to deliver evidence for physics beyond the Standard Model. One…
The anticipated physics program at an high energy e+e- linear collider places special emphasis on the accuracy in extrapolating charged particle tracks to their production vertex to tag heavy quarks and leptons. This paper reviews physics…
Use of machine learning to estimate nuisance functions (e.g. outcomes models, propensity score models) in estimators used in causal inference is increasingly common, as it can mitigate bias due to model misspecification. However, it can be…
We cover some current topics in Beyond the Standard Model phenomenology, with an emphasis on collider (particularly Large Hadron Collider) phenomenology. We begin with a review of the Standard Model and some unresolved mysteries that it…
We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the…
All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and…
Integrative modeling of macromolecular assemblies allows for structural characterization of large assemblies that are recalcitrant to direct experimental observation. A Bayesian inference approach facilitates combining data from…
Recent years have seen a significant increase in the use of machine intelligence for predicting electronic structure, molecular force fields, and the physicochemical properties of various condensed systems. However, substantial challenges…
Important analyses at a future linear collider, including top-quark and W-boson mass measurements will depend upon the precise determination of the luminosity spectrum. This can be done, in principle,in the planned detectors. We review the…
In this paper we present updated constraints on the top-quark sector of the Standard Model Effective Field Theory using data available from Tevatron, LEP and the LHC. Bounds are obtained for the Wilson coefficients from a global fit…
Information deformation and loss in jet clustering are one of the major limitations for precisely measuring hadronic events at future $e^-e^+$ colliders. Because of their dominance in data, the measurements of such events are crucial for…
Exploring the leptonic sector in frontier experiments is more of importance nowadays, since the conservation of lepton flavor and total lepton number are not guaranteed anymore in the Standard Model after the discovery of neutrino…
The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of…