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10-15 March 2019
Steinmatte conference center
Europe/Zurich timezone

Physics inspired feature engineering with Lorentz Boost Networks

12 Mar 2019, 19:00
Steinmatte Room A

Steinmatte Room A

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


Yannik Alexander Rath (RWTH Aachen University (DE))


A large part of the success of deep learning in computer science can be attributed to the introduction of dedicated architectures exploiting the underlying structure of a given task. As deep learning methods are adopted for high energy physics, increasing attention is thus directed towards the development of new models incorporating physical knowledge.

In this talk, we present a network architecture that utilizes our knowledge of particle combinations and directly integrates Lorentz boosting to learn relevant physical features from basic four vectors. We explore two example applications, namely the discrimination of hadronic top-quark decays from light quark and gluon jets, and the separation of top-quark pair associated Higgs boson events from a $t\bar{t}$ background. We also investigate the learned combinations and boosts to gain insights into what the network is learning.

Primary authors

Martin Erdmann (Rheinisch-Westfaelische Tech. Hoch. (DE)) Mr Erik Geiser (III. Physics Institute A - RWTH Aachen University) Yannik Alexander Rath (RWTH Aachen University (DE)) Marcel Rieger (Rheinisch-Westfaelische Tech. Hoch. (DE))

Presentation materials

Peer reviewing