Session 6: Particle and Nuclear Physics
Sep 21, 2021 (updated Oct 11, 2021)
AI in High Energy Physics (HEP) and Nuclear Physics (NP) represents the next generation of methods to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance research in fundamental physics. These methods include (but are not limited to) machine learning (ML) and deep learning (DL). ML techniques have a long history in HEP. With the advent of modern DL networks, their use expanded widely and is now ubiquitous to both HEP and NP, as found promising for many different purposes like anomaly detection, event classification, simulations, or the design and operation of large-scale accelerator facilities and experiments.
Time
Wednesday, October 13, 2021 14:30 - 16:00 UTC (click for other timezones)
Panelists
- Cristiano Fanelli, Nuclear physics (experiment) ,MIT
- Fernanda Psihas, HEP (experiment), FNAL
- Gregor Kasieczka, HEP (experiment), Hamburg
- Tanja Horn, Nuclear physics (experiment), CUA
Conveners
- Jennifer Ngadiuba, Fermi National Accelerator Laboratory (Fermilab)
- Markus Diefenthaler, Thomas Jefferson National Accelerator Facility (JLab)
Contributions
- Karthik Suresh: EIC-ECCE Detector Design Optimization with AI
- Ronglong Fang: Online Multiscale Method for Change Detection in Automated Data-Quality Monitoring
- Davide Di Croce: Machine Learning at CMS
- Yasir Alanazi: Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
- William Korcari: Shared Data and Algorithms for Deep Learning in Fundamental Physics
- Manal Almaeen: Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Framework for Inverse Problems
- Jason St. John: On-Chip ML Control Agent for Precision Regulation at the Fermilab Booster Synchrotron
- Jack Araz: Quantum-inspired event reconstruction with Tensor Networks
- Felix Wagner: A Python Package with Novel Raw Data Analysis Methods for Cryogenic Particle Detectors
- Arno Straessner: Artificial Neural Networks on FPGAs for Real-Time Energy Reconstruction of the ATLAS LAr Calorimeters