Please visit Jefferson Lab Event Policies and Guidance before planning your next event: https://www.jlab.org/conference_planning.

May 8 – 12, 2023
Norfolk Waterside Marriott
US/Eastern timezone

Use of Anomaly Detection algorithms to unveil new physics in Vector Boson Scattering

Not scheduled
1h
Hampton Roads Ballroom and Foyer Area (Norfolk Waterside Marriott)

Hampton Roads Ballroom and Foyer Area

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Poster Poster Poster Session

Speaker

Lavizzari, Giulia (INFN and University of Milano-Bicocca)

Description

new methodology to improve the sensitivity to new physics contributions to the Standard Model processes at LHC is presented.

A Variational AutoEncoder trained on Standard Model processes is used to identify Effective Field Theory contributions as anomalies. While the output of the model is supposed to be very similar to the inputs for Standard Model events, it is expected to deviate significantly for events generated through new physics processes. The reconstruction loss can then be used to select a signal enriched region which is by construction independent of the nature of the chose new physics process. In order to improve further the discrimination power, an adversarial layer is introduced with a cross entropy term added to the loss function, optimizing at the same time the reconstruction of the input variables of the Standard Model and classification of new physics processes. This procedure ensures that the model is optimized for discrimination, with a small price in terms of model dependency to physics process.

In this seminar I will discuss in detail the above-mentioned method using generator level Vector Boson Scattering events produced at LHC assuming an integrated luminosity of 350/𝑓𝑏.

Consider for long presentation No

Primary authors

Lavizzari, Giulia (INFN and University of Milano-Bicocca) Gennai, Simone Govoni, Pietro (INFN and University of Milano-Bicocca) Boldrini, Giacomo (INFN and University of Milano-Bicocca)

Presentation materials

Peer reviewing

Paper