Speaker
Ines Ochoa
(LIP Laboratorio de Instrumentacao e Fisica Experimental de Particulas (PT))
Description
ABSTRACT:
In recent years, there have been many proposed methodologies for machine learning anomaly detection at the LHC, such as those reported in the LHC Olympics and Dark Machines community reports. The first search using machine-learning anomaly detection was performed by ATLAS in the dijet final state, a fully data-driven analysis that uses the Classification Without Labels method and is complementary to the existing dedicated resonance searches.
In this talk, I will use the experience gained with the ATLAS weakly supervised dijet analysis to discuss the general
challenges of doing anomaly detection with LHC data and methodologies to address them.
