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

Controlling Quality for a Physics-Driven Generative Models and Auxiliary Regression Approach

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

Ratnikov, Fedor (HSE University)

Description

High energy physics experiments heavily rely on the results of MC simulation of data used to extract physics results. However, the detailed simulation often requires tremendous amount of computation resources.

Using Generative Adversarial Networks and other deep learning generative techniques can drastically speed up the computationally heavy simulations like a simulation of the calorimeter response. To be useful, such models are required to satisfy quality metrics which are driven by a specific physics properties of generated objects rather than by a regular ML image-like quality metrics.

The auxiliary regression extension to the GAN-based fast simulation demonstrated improvements of the physics quality for generated objects. This approach introduces physics metrics to a Discriminator path of the model thus allows direct discriminating of objects with poorly reproduced properties.

In this presentation we discuss the auxiliary regression GAN approach to physics-based fast simulation and concentrate on requirements to the quality of the auxiliary regressor to provide a necessary precision of the generative models built on top of this regressor.

Consider for long presentation No

Primary authors

Rogachev, Alexander (HSE University) Ratnikov, Fedor (HSE University)

Presentation materials

Peer reviewing

Paper