Aug 21 – 25, 2017
University of Washington, Seattle
US/Pacific timezone

How easily can neural networks learn relativity?

Aug 24, 2017, 5:50 PM
107 (Alder Hall)


Alder Hall

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


Kartik Chitturi (University of Texas (US))


We study the ability of different deep neural network architectures to learn various relativistic invariants and other commonly-used variables, such as the transverse momentum of a system of particles, from the four-vectors of objects in an event. This information can help guide the optimal design of networks for solving regression problems, such as trying to infer the masses of unstable particles produced in a collision.

Primary authors

Kartik Chitturi (University of Texas (US)) Peter Onyisi (University of Texas (US))

Presentation materials

Peer reviewing