November 29, 2021 to December 3, 2021
Virtual and IBS Science Culture Center, Daejeon, South Korea
Asia/Seoul timezone

Unsupervised Machine Learning for New Physics Searches

contribution ID 789
Dec 3, 2021, 4:50 PM
30m
Auditorium (Virtual and IBS Science Culture Center, Daejeon, South Korea)

Auditorium

Virtual and IBS Science Culture Center, Daejeon, South Korea

55 EXPO-ro Yuseong-gu Daejeon, South Korea email: library@ibs.re.kr +82 42 878 8299

Speaker

Michael Spannowsky (University of Durham (GB))

Description

In the absence of new physics signals and in the presence of a plethora of new physics scenarios that could hide in the copiously produced LHC collision events, unbiased event reconstruction and classification methods have become a major research focus of the high-energy physics community. Unsupervised machine learning methods, often used as anomaly-detection methods, are trained on Standard Model processes and should indicate if a collision event is irreconcilable with the kinematic features of Standard Model events. I will briefly review popular unsupervised neural network methods proposed for the analysis of high-energy physics collision events. Further, I will discuss how physics principles can guide such methods and how their susceptibility to systematic uncertainties can be curbed.

Presentation materials