Learning to Discover

Session

Generative Models workshop

GEN

25 avr. 2022, 09:00

Institut Pascal

Présidents de session

Generative Models workshop: Monday morning

• Il n'a pas de président de session pour ce bloc

Generative Models workshop: Monday afternoon

• Cécile Germain (LISN, Université Paris-Saclay)

Generative Models workshop: Tuesday morning

• Il n'a pas de président de session pour ce bloc

Generative Models workshop: Tuesday afternoon

• Il n'a pas de président de session pour ce bloc

65. Generator Workshop introduction

25/04/2022 09:00

66. Overview of generator model for detector simulation

Johnny Raine (Université de Genève)

25/04/2022 09:15

Detailed simulations of physics processes are a cornerstone of most physics measurements and searches at experiments such as those at the Large Hadron Collider (LHC) as well as in other scientific domains. However, the large volumes of data collected at the LHC precludes a requirement for even more simulated data in order to test various hypotheses to a high precision. As the LHC moves to the...

95. Generator model for 4-momenta events

Sascha Diefenbacher (Universität Hamburg)

25/04/2022 10:40

Event generation and Hadronization make up a significant fraction of our high energy physics simulation chain. Therefore there exists a strong interest in using generative models to supplement these simulation tasks. In both applications the data is commonly expressed as a list of 4-momenta. This presents an additional challenge to any generative model, as 4-momenta have inherent complex...

93. Break out session in the small amphi

25/04/2022 11:30

94. GNNs for generating molecules and PDE solutions

Max Welling (U. Amsterdam / MSR)

25/04/2022 14:00

Graph Neural Networks (GNNs) have proven to be a versatile tool to predict properties of molecules, generative molecules and even predict solutions of a partial differential equation (PDE). Many physical application domains also exhibit symmetries which can be incorporated into the GNNs through equivariant convolutions or data augmentation. In this talk I will explain how this tool can be...

69. Breakout sessions

25/04/2022 16:00

67. The calorimeter challenge and ODD detector

Anna Zaborowska (CERN)

26/04/2022 09:00

There has been many recent developments in the application of machine-learning techniques to fast simulation of cascades in the calorimeters. This is usually the most time consuming part of the event simulation in high energy physics experiments. Most current efforts are focused and fine tuned to specific detectors, which makes it difficult to compare. We present a first fast calorimeter...

71. Breakout sessions

26/04/2022 11:00

98. Meta-learning for fast simulation of multiple calorimeter responses

Dalila Salamani (CERN)

26/04/2022 14:00

In LHC experiments, the calorimeter is a key detector technology to measure the energy of particles. These particles interact electromagnetically and/or hadronically with the material of the calorimeter, creating cascades of secondary particles or showers. Describing the showering process relies on simulation methods that precisely describe all particle interactions with matter. Constrained by...

72. Breakout sessions

26/04/2022 14:20

68. Breakout sessions

70. Breakout sessions

73. Conclusion from breakout sessions