Publications de Andrea Wulzer | EPFL Graph Search

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Boosting likelihood learning with event reweighting

Andrea Wulzer, Alfredo Glioti, Siyu Chen

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Springer2024

Learning from radiation at a very high energy lepton collider

Riccardo Rattazzi, Andrea Wulzer, Alfredo Glioti, Siyu Chen, Lorenzo Ricci

We study the potential of lepton collisions with about 10 TeV center of mass energy to probe Electroweak, Higgs and Top short-distance physics at the 100 TeV scale, pointing out the interplay with the long-distance (100 GeV) phenomenon of Electroweak radia ...

SPRINGER2022

Learning new physics from an imperfect machine

Raffaele Tito D'Agnolo, Andrea Wulzer

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties a ...

SPRINGER2022

Two paths towards precision at a very high energy lepton collider

Andrea Wulzer, Roberto Franceschini

We illustrate the potential of a very high energy lepton collider (from 10 to 30 TeV center of mass energy) to explore new physics indirectly in the vector boson fusion double Higgs production process and in direct diboson production at high energy. Double ...

SPRINGER2021

High-energy EFT probes with fully differential Drell-Yan measurements

Andrea Wulzer, Lorenzo Ricci

We study the potential of fully-differential measurements of high-energy dilepton cross-sections at the LHC to probe heavy new physics encapsulated in dimension-6 interaction operators. The assessment is performed in the seven-dimensional parameter space o ...

SPRINGER2021

Learning multivariate new physics

Raffaele Tito D'Agnolo, Andrea Wulzer

We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any prior assumption on the nature of the deviation. It is designed to be sensi ...

SPRINGER2021

Parametrized classifiers for optimal EFT sensitivity

Andrea Wulzer, Alfredo Glioti, Siyu Chen

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 leptonic decays, modeled ...

SPRINGER2021

Goldstone equivalence and high energy electroweak physics

Andrea Wulzer, Gabriel Francisco Cuomo, Luca Vecchi

The transition between the broken and unbroken phases of massive gauge theories, namely the rearrangement of longitudinal and Goldstone degrees of freedom that occurs at high energy, is not manifestly smooth in the standard formalism. The lack of smoothnes ...

2020

Learning new physics from a machine

Raffaele Tito D'Agnolo, Andrea Wulzer

We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particu ...

AMER PHYSICAL SOC2019

Andrea Wulzer

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Boosting likelihood learning with event reweighting

Learning from radiation at a very high energy lepton collider

Learning new physics from an imperfect machine

Two paths towards precision at a very high energy lepton collider

High-energy EFT probes with fully differential Drell-Yan measurements

Learning multivariate new physics

Parametrized classifiers for optimal EFT sensitivity

Goldstone equivalence and high energy electroweak physics

Muon Collider. A Path to the Future?

Learning new physics from a machine

Boosting likelihood learning with event reweighting

Two paths towards precision at a very high energy lepton collider

High-energy EFT probes with fully differential Drell-Yan measurements

Goldstone equivalence and high energy electroweak physics

Learning from radiation at a very high energy lepton collider

Learning multivariate new physics

Muon Collider. A Path to the Future?

Parametrized classifiers for optimal EFT sensitivity

Learning new physics from a machine

Learning new physics from an imperfect machine