Over the past 15 years, High Energy Physics (HEP) has made significant strides by developing and applying machine learning (ML) and artificial intelligence (AI) approaches. These advancements have greatly improved particle and event identification, reconstruction, simulation, experiments operations, and more.
The workshop will highlight the latest progress and ongoing challenges in these areas. It is open to the entire community, including participants from LHC experiments (detector and accelerator), theorists, and phenomenologists. We welcome contributions from method scientists and experts in related fields such as astronomy, astrophysics, cosmology, astroparticle physics, hadron and nuclear physics, and other domains facing similar challenges as well as computer scientists in research, industry and academia.
Join us to explore new ideas and advance the future of particle physics and science through ML/AI.
The following topics accross the various fields are foreseen:
- Classification and reconstruction
- Experiment simulation
- Event generation
- Inverse problems
- Uncertainties
- Anomaly detection
- Interpretability
- Detector/accelerator monitoring, control, and data acquisition
Abstract submission is open now and will close on May 13th 23:59 US/Pacific time.
The workshop will be organised in a hybrid format (with a Zoom connection option). We expect speakers to attend in-person.
Join the ML4Jets Slack Channel for discussions.
Local organizing committee:
Maria Spiropulu (Caltech) – chair
Jean-Roch Vlimant (Caltech)
Jennifer Ngadiuba (Fermilab/Caltech)
Raghav Kansal (Caltech/Fermilab)
Abhijith Gandrakota (Fermilab)
Nhan Tran (Fermilab)
Javier Duarte (UCSD)
Hongkai Zheng (Caltech)
Pietro Perona (Caltech)
Joe Lykken (Fermilab)
Katie Bouman (Caltech)
International Advisory Committee:
Florencia Canelli (University of Zurich)
Kyle Cranmer (UW-Madison)
Vava Gligorov (LPNHE)
Gian Michele Innocenti (CERN)
Gregor Kasieczka (Universität Hamburg)
Ben Nachman (LBNL)
Mihoko Nojiri (KEK)
Maurizio Pierini (CERN)
Tilman Plehn (Heidelberg)
David Shih (Rutgers)
Jesse Thaler (MIT)
Sofia Vallecorsa (CERN)
