Learn · CERN

Talk14 min

Machine learning for surface prediction in ACTS

Benjamin Huth · CERN

Watch At CERN

About

We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit..

0:00:00 Slide 1

0:01:22 Slide 2

0:02:44 Slide 3

0:04:06 Slide 4

0:05:28 Slide 5

0:06:50 Slide 6

0:08:12 Slide 7

0:09:34 Slide 8

0:10:56 Slide 9

0:12:18 Slide 10

0:13:40 Slide 11

The talk

  1. Machine learning for surface prediction in ACTS 2021-05-19 · 14 min · CERN's player

Credits and licence

Institution
CERN
Department
Conferences
Speaker
Benjamin Huth
Year
2021
Language
English
Subjects
Physics, Computer science, Earth and environment
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
Who can watch
CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed