Publications by Andrea Wulzer | EPFL Graph Search

Boosting likelihood learning with event reweighting

Andrea Wulzer, Siyu Chen, Alfredo Glioti

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 ...

Springer Nature2024

Muon Collider Forum report

Théodora Cohen Liechti, João Miguel das Neves Duarte, Andrea Wulzer, Wei Li, Xiao Wang, Christoph Herwig

A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique ...

Iop Publishing Ltd2024

Towards a muon collider (vol 83, 864, 2023)

Riccardo Rattazzi, Varun Sharma, Siyu Chen, Xavier Buffat, Brian Quinn Henning, Filippo Nardi, Niko Neufeld, Luca Bottura, Andrea Wulzer, Roberto Franceschini

Springer2024

Towards a muon collider

Riccardo Rattazzi, Varun Sharma, Siyu Chen, Xavier Buffat, Brian Quinn Henning, Filippo Nardi, Niko Neufeld, Luca Bottura, Andrea Wulzer, Roberto Franceschini

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by ...

Springer2023

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 ...

Springer Nature2022

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

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 ...

Springer Nature2021

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 ...

Springer Nature2021

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 ...

Springer Nature2021