About
High-energy physics (HEP) experiments rely on Monte Carlo (MC) simulation to test hypotheses and understand data distributions. To meet the demand for fast and large-scale simulated samples, novel Fast Simulation techniques have emerged, including neural network models. This project explores the application of a large-scale transformer-based model, leveraging recent advancements in foundation models (e.g., GPT-3, Dall-E-2), for fast simulation in HEP. My contribution involves working on preprocessing and loss function design to enhance the efficiency and accuracy of the proposed transformer-based model and compare their results with other existing methodologies..
0:00:00 Slide 1
0:00:15 Slide 2
0:00:30 Slide 3
0:00:44 Slide 4
0:00:59 Slide 5
0:01:13 Slide 6
0:01:28 Slide 7
0:01:42 Slide 8
0:01:57 Slide 9
0:02:12 Slide 10
0:02:26 Slide 11
The talk
Credits and licence
- Institution
- CERN
- Department
- CERN openlab Summer Student Programme 2023
- Speaker
- Zeeshan Memon
- Year
- 2023
- Language
- English
- 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 Teens profiles and older, judged from what the source says about it. How courses are placed