# Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics

- Institution: CERN
- Department: Conferences
- Speaker: Ali Hariri
- Year: 2021
- Language: English
- Subjects: Physics, Computer science, Earth and environment
- Source: [CERN](https://videos.cern.ch/record/3008093)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with the detector is both time consuming and computationally expensive. With its proton-proton collision energy of 13 TeV, the Large Hadron Collider is uniquely positioned to detect and measure the rare phenomena that can shape our knowledge of new interactions. The High-Luminosity Large Hadron Collider (HL-LHC) upgrade will put a significant strain on the computing infrastructure and budget due to increased event rate and levels of pile-up. Simulation of high-energy physics collisions needs to be significantly faster without sacrificing the physics accuracy. Machine learning approaches can offer faster solutions, while maintaining a high level of fidelity. We introduce a graph generative model that provides effective reconstruction of LHC events on the level of calorimeter deposits and tracks, paving the way for full detector level fast simulation..

0:00:00 Slide 1

0:01:33 Slide 2

0:03:06 Slide 3

0:04:38 Slide 4

0:06:11 Slide 5

0:07:43 Slide 6

0:09:16 Slide 7

0:10:48 Slide 8

0:12:21 Slide 9

0:13:53 Slide 10

0:15:26 Slide 11

## Lectures

1. [Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics](https://invisiblenetwork.ai/learn/cern/3008093/943cfb88eeb8), 16 min (CERN's player)