# Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment

- Institution: CERN
- Department: Conferences
- Speaker: Victor Goicoechea Casanueva
- Year: 2021
- Language: English
- Subjects: Physics, Computer science, Earth and environment
- Source: [CERN](https://videos.cern.ch/record/3008118)
- 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)

While deep learning techniques are becoming increasingly more popular in high-energy and, since recently, neutrino experiments, they are less confidently used in direct dark matter searches based on dual-phase noble gas TPCs optimized for low-energy signals from particle interactions. In the present study, application of modern deep learning methods for event ver- tex reconstruction is demonstrated with an example of the 50-tonne liquid argon DarkSide-20k TPC with almost 10 thousand photosensors. The developed methods successfully reconstruct event's position withing sub- cm precision and are applicable to any dual-phase argon or xenon TPC of arbi- trary size with any sensor shape and array pattern..

0:00:00 Slide 1

0:01:09 Slide 2

0:02:17 Slide 3

0:03:25 Slide 4

0:04:33 Slide 5

0:05:42 Slide 6

0:06:50 Slide 7

0:07:58 Slide 8

0:09:06 Slide 9

0:10:14 Slide 10

0:11:23 Slide 11

## Lectures

1. [Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment](https://invisiblenetwork.ai/learn/cern/3008118/82a47917f5ae), 12 min (CERN's player)